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Too often, as designers and developers we fail to make interfaces that are usable, fail to make software that is intuitive, and fail to make products that normal people can understand. By coating design rigour with a layer of brand fluff, and putting form over function again and again, we build products that serve nobody but the internal needs of our corporations and brands. In this interview with Will Grant, a web technology entrepreneur and veteran, we discuss ways to solve 101 UX design problems clearly and single-mindedly. We also discuss about his upcoming book 101 UX Principles, in which Will has defined and refined what it means to build products people intuitively know how to use. Author’s Bio Will Grant is a British UI/UX expert and graduate of Birmingham City University, where he studied human computer interaction and usable design. Following his degree, he trained with Jakob Nielsen and Bruce Tognazzini, pioneers in UX design. Will has been building intuitive usable software products since the birth of the consumer web over 20 years ago, through to the present day, where Will's work has reached more than a billion users. He is the co-founder and the design lead at UX-focused analytics tool Prodlytic. Key takeaways: The vast majority of UX is still about concepts, journey and the tasks we help users to achieve. The tools to deliver great UX have changed, but UX is still about familiarity, consistency and empathy. The 101 UX Principles are a shortcut for UX professionals. Designers can apply them to their products and make usable software 99% of the time for 99% of users. Over reliance on ‘brand’ and internal goals, trying to reinvent the wheel, and forgetting to put oneself in the place of the user are some common reasons why UX design fails. Many UX people forget that design – UI design in particular – isn’t art, it’s design to perform a function: to serve users. Follow Will’s 10 commandments for effective UX design to create more usable and successful products. There’s another 91 in the book 101 UX Principles too. Full Interview Of the 100+ UX design principles that you explore in your new book, if we asked you to pick the top 10, what would those be? Will’s 10 commandments for effective UX design, so to speak. Test with real users Don’t join the dark side Make your buttons look like buttons Label your icon buttons Use 2 font families, maximum Make ‘blank slates’ more than just empty views Hide ‘advanced’ settings from most users Decide if an interaction should be obvious, easy, or possible Anyone can be a UX professional Use device-native input features where possible Just following these 10 and applying them to your software design will create more usable, successful products. There’s another 91 great commandments in the book too. Will, as this book is about 101 UX Principles, what makes your principles right? Nothing is perfect, but these principles are a ‘shortcut’ for UX professionals. Instead of reinventing the wheel, designers can apply these principles to their products and make usable software 99% of the time for 99% of users. I’ve spent over 20 years, since the birth of the consumer web, building interfaces for 100s of products and over a billion users. My approach isn’t perfect, but it has been tested and proven to work at scale. This guide will help you avoid common mistakes and start with a product that’s extremely usable and intuitive - for the widest possible section of users. Why do people keep making UX mistakes? It’s usually a combination of factors; over reliance on ‘brand’ and internal goals, trying to reinvent the wheel, and forgetting to put yourself in the place of the user. Too often the internal goals of an organisation supercede the design teams who are genuinely trying to ‘fight for the user’. The CEO wants it to look a certain way (but he/she has no design background), or the marketing team decide that a certain typeface has to be used (even though it’s unreadable). The paradox is that, as UX and UI people, we’re over-exposed to components, controls, patterns and interfaces in general. It’s the curse of knowledge and we are the last people who should be designing interfaces — unless we can do the hard bit: objectivity. Name a big company that gets UX right, and one that gets it wrong This is impossible, even today after 20+ years of consumer web products, the experience people see is wildly different from product to product - regardless of the company. Generally, large companies with lots of internal bureaucracy and hierarchy produce end products that are the least usable - this is where small, nimble startups can often produce a better product: not because they are ‘better’ overall, but because they haven’t yet lost sight of the importance of UX. And, crucially, startup teams are less encumbered by legacy baggage and are more free to follow best practice in design. Who inspires you the most with the UX community? Donald Norman & Jakob Nielsen have both been hugely influential to me. Don Norman’s book “The design of everyday things”  pretty much kicked off and ‘invented’ the whole field of human-computer interaction, which these days we call ‘UX’. Nielsen & Norman are sometimes derided as ‘too purist’ but that’s what appeals to me most. Stripping back interfaces to the bare minimum, removing clutter and making things simple are things I try to do in my work every day. I worked for a boss in my early 20’s - he wasn’t a designer - but he did fly into an apoplectic rage at the slightest mistake I might make. It taught me to check, check and re-check my designs and despite him being a horrible person, my work is better for it. What was the last app that made you throw down your phone in frustration? Easy - it was the HSBC app, yesterday, with it’s dreadful ‘update’ process. Apple have gone to great lengths to build an App Store which auto-updates your apps, in the background while you’re asleep and your phone is on charge. HSBC decided that their banking app should do its own half-assed updates, whenever it feels like it, inside the app - just when you open the app and you’re about to use it. A classic example of reinventing the wheel, building a new experience that fails because nobody has thought of the user - only of their internal needs. In your more than two decades of UX design experience, how has the web evolved from a user experience perspective? What were some of the biggest surprises in UX design trends for you? What design ideas have remained unaltered by time? I think it’s remarkable how little has changed - in terms of design ideas that ‘just work’ at least. Yes, software has changed massively over that time - from basic websites and browsers on desktop computers through to web app and native apps on smartphones and tablets. However, the vast majority of UX is still about the concepts, the journey and the tasks you’re helping the user to achieve. The tools to deliver great UX have changed, but UX itself is still about familiarity, consistency and empathy. With emerging technologies like machine learning, AR, VR, IoT etc increasingly impacting how we design for the web, where do you see UX design heading in the coming years? What are some general rules worth keeping in mind when designing for the future? What are some opportunities and challenges you foresee for UX designers? It's more of a hope than a prediction, but perhaps us designers will stop doing things because we can and start asking if we should. A greater sense of social responsibility, and a reduction in sneaky 'dark pattern' UX would be great for everyone. Somewhere along the way, many UX people forgot that design – UI design in particular – isn’t art, it’s design to perform a function: to serve users. Too many designers are slavishly following the latest design trend, applying ‘flat design’ to every app, or trying to be different for the sake of it, with custom-designed interfaces and arbitrary visual metaphors. The solution is simple, too: try and be objective, fight for the user, and test with real users as you go. 101 UX Principles provides 101 ways to solve 101 UX problems clearly and single-mindedly. There are 1000s of methods to apply to each and every interaction in your product, but this book is a ‘shortcut’ to a method that works. The book is available to pre-order now and is expected to be published soon. What UX designers can teach Machine Learning Engineers? To start with: Model Interpretability Is your web design responsive? A UX strategy is worthless without a solid usability test plan    

Serverless computing is a growing trend that lets software developers focus more on code than the back-end processes. While there are a lot of serverless computing platforms, in this article we will focus on Microsoft’s Azure serverless computing platform, which provides its users with  fully managed, end-to-end Azure serverless solutions to boost developer productivity, optimise resources and expedite the development processes. To understand the nitty-gritties of Azure Serverless, we got in touch with Lorenzo Barbieri, a cloud-native application specialist who works at Microsoft’s One Commercial Partner Technical Organization and, Massimo Bonanni, an Azure Technical trainer at Microsoft. In their recently published book, Mastering Azure Serverless Computing, they explain how developers with Microsoft’s Azure Serverless platform can build scalable systems and also deploy serverless applications with Azure Functions. Sharing their thoughts about Azure serverless and its security the authors said that although security is one of the most important topics while designing a complex solution, security depends both on the cloud infrastructure as well as the code. They further shared how Powershell in Azure Functions allows you to combine the best language for automation with one of the best services. Sharing their experiences working at Microsoft, they also talked about how their recently published book will help developers master various processes in Azure serverless. On how Microsoft ensures complete security within the Serverless Computing process Every architecture should guarantee a secure environment for the user. Also, the security of any Serverless functions depends on the cloud provider's infrastructure, which may or may not be secure. What are the certain security checks that Microsoft ensures for complete security within the Serverless Computing processes? Lorenzo: Security of Serverless functions depends both on the cloud provider’s infrastructure and the application code. For example,  SQL Injections depends on how the application code is written; you should check all the inputs (depending on the trigger) to avoid these types of attacks. Many other types of attacks depend on application code and third party dependencies. On its side, Microsoft is responsible for managing and patching servers and application frameworks, and keeps them updated when security updates are released. .” Massimo: Security is one of the most important topics when you design a complex solution, and in particular, when it will run on a cloud provider. You must think about it from the beginning of your design. Azure provides a series of ot-of-the-box services to ensure the security of the solutions that you deploy on it. For example, Azure DDoS Protection Service is an Azure service you have for free on every solution you deploy, and especially if you are developing Azure Functions triggered by HTTP trigger. On the other hand, you must guarantee that your code is safe and that your third party dependencies are secure too. If one of the actors of your solution chain is unsafe, all your solution becomes potentially not secure. On general availability of PowerShell in Azure Functions V2 The Microsoft team recently announced the general availability of PowerShell in Azure Functions V2. Azure Functions is known for its speed and PowerShell for its automation; how will this feature enhance serverless computing on Azure Cloud? What benefits can users or organizations expect with this feature? What does this mean for Azure developers? Lorenzo: GA of PowerShell in Azure Functions is a great news for cloud administrators and developers that can use them connected for example with Azure Monitor alerts, to create custom auto-scale rules or to implement mitigation for problems that could arise. Massimo: Serverless architecture gives its best for event-driven solutions. Automation in Azure is, generally, driven by events generated by the platform. For example, you have to do something when someone creates a storage, or you have to execute a task every hour. Using Powershell in an azure function allows you to combine the best language for automation with one of the best services to react to events. On why developers should prefer Azure Serverless computing Can you tell us some of the pre-requisites expected before reading your book? How does your book prepare its readers to master Azure Serverless Computing and to be industry ready? Lorenzo: A working knowledge of .NET or other programming languages is expected, together with basic understanding of Cloud architectures. For Chapter 7 [Serverless and Containers], basic knowledge of containers and Kubernetes is expected. The book covers all the advanced features of Azure Serverless Computing, not only Azure Functions. After reading the book, one can decide which technology to use. Massimo: The book supposes that you have a basic knowledge of programming language (e.g. C# or Node.js) and a basic knowledge of Cloud topics and architecture. Moreover, for some chapters (e.g., Chapter 7), you need some other knowledge like containers and Kubernetes. In your book, ‘Mastering Azure Serverless Computing’, you have said that Containers and Orchestrators are the main competitors of Serverless in terms of Architecture. What makes Serverless architecture better than the other two? How does one decide while migrating from a monolith, which architecture to adopt? What are some real-world success stories of serverless migration? Lorenzo: In Chapter 7 we’ve seen that it’s possible to create Containers and run them inside Azure Functions, and that’s also possible to run Azure Functions inside Kubernetes, AKS or OpenShift together with KEDA. The two worlds are not mutually exclusive, but most of the times you choose one route or another. Which one you should use? Serverless is more productive, it’s really easy to scale and it’s better suited for event-driven architectures. With Orchestrators like Kubernetes you can customize every aspect of your infrastructure, you can create complex service connections and dependencies, and you can deploy them everywhere. Stylelabs, a leading Belgium/US-based marketing software company, successfully integrated Azure Functions into its cloud architecture to benefit from serverless in addition to traditional solutions like VMs and App Services. Massimo: I think that there isn't a better tool to implement something. As I always say during my technical sessions (even if I seem repetitive and boring), when you choose an architecture (e.g. microservices or serverless), you choose it because that architecture meets the requirements of the solution you are designing. If you choose an architecture because it is popular or "fashionable", you are making a serious mistake that you will pay when your solution will be deployed. In particular, Microservice architecture (that you can implement using Container and Orchestrator) and Serverless architecture meet different requirements (e.g. Serverless is the best solution when you need an event-driven architecture while one of the most important characteristics of the microservices architecture is high availability and orchestration), so I think they can be used together. A few highlights of Microsoft Azure Functions What are the top 5 highlights of Azure Functions that make it a go-to serverless platform for newbies and professionals? Massimo: For the Azure Functions, the five best features are, in my opinion: Support for a number of programming languages and also has the possibility to support any other programming languages, which are not currently available; Extensibility of triggers and bindings to support your custom data sources; Availability of a number of tools available to implement Azure Functions (Visual Studio, Visual Studio Code, Azure Functions Tools, etc., etc.); Use of the open-source approach for runtime and tools; Capability to easily use Azure Functions with other Azure services such as Event Grid or Azure Key Vault. Lorenzo and Massimo on their personal experiences working with Microsoft Azure services Lorenzo, you have a specialization in Cloud Native Applications and Application Modernization. Can you share your experience and the challenges you faced with the Cloud-native learning curve? You have also been using Azure Functions since the first previews. How has it grown from the first preview? In the beginning it was difficult. Azure includes many services and it’s growing even faster. In the beginning, I simply tried to understand the big picture of the services and their relationship. Then I started going deeper in the services that I needed to use. I’m thankful to many highly skilled colleagues, who started this journey before me. I can say that two years of working with Azure and the experience you gain is the minimum time to master the parts that you need. Speaking of Azure Functions, the first preview was interesting, but limited. Azure Functions v2 and the upcoming v3 are great platforms, both in terms of features and in terms of scalability, and configuration. Massimo, you are an Azure Technical Trainer at Microsoft, can you share with us your journey with Microsoft. What were the projects you enjoyed being involved in? Where do you see microservice and serverless architecture in the next five years? During my career, I have always worked with Microsoft technologies and have always wanted to be a Microsoft employee. For several years I was a Microsoft MVP, and, finally, three years ago, I was hired. Initially, I worked for the business unit that provides consulting to customers and partners for implementing solutions (not only Cloud oriented). In almost three years of consulting, I worked on various projects for different customers and partners with different Azure technologies, specially Microservice architecture, and during the last year, serverless. I think that these two architectures will be the most important in the next years specially for enterprise solutions. When you are a consultant, you are involved in a lot of projects, and every project has its peculiarity and its problems to solve, and it isn't simple to remember all of them. The most important thing that I learned during these years, is that those who design solutions for the Cloud must be like a Chef: you can use different ingredients (the various services offered by the Cloud) but must mix them in the right way to get the right recipe. Since three months, I am an Azure Technical Trainer, and I help our customers to better understand Azure services and use the right one in their solutions. About the Authors Lorenzo Barbieri Lorenzo Barbieri works for Microsoft, in the One Commercial Partner Technical Organization, helping partners, developers, communities, and customers across Western Europe, supporting software development on Microsoft and OSS technologies. He specializes in cloud-native applications and application modernization on Azure and Office 365, Windows and cross-platform applications, Visual Studio, and DevOps, and likes to talk with people and communities about technology, food, and funny things. He is also a speaker, trainer, and a public speaking coach and has helped many students, developers, and other professionals, as well as many of his colleagues, to improve their stage presence with a view to delivering exceptional presentations. Massimo Bonanni Massimo Bonanni is an Azure technical trainer in Microsoft and his goal is to help customers utilize their Azure skills to achieve more and leverage the power of Azure in their solutions. He specializes in cloud application development and, in particular, in Azure compute technologies. Over the last 3 years, he has worked with important Italian and European customers to implement distributed applications using Service Fabric and microservices architecture. Massimo is also a technical speaker at national and international conferences, a Microsoft Certified Trainer, a former MVP (for 6 years in Visual Studio and Development Technologies and Windows Development), an Intel Software Innovator, and an Intel Black Belt. About the book Mastering Azure Serverless Computing will guide you through using Microsoft's Azure Functions to process data, integrate systems, and build simple APIs and microservices. You will also discover how to apply serverless computing to speed up deployment and reduce downtime. You'll also explore Azure Functions, including its core functionalities and essential tools, along with understanding how to debug and even customize Azure Functions. “Microservices require a high-level vision to shape the direction of the system in the long term,” says Jaime Buelta Glen Singh on why Kali Linux is an arsenal for any cybersecurity professional [Interview] Why become an advanced Salesforce administrator: Enrico Murru, Salesforce MVP, Solution and Technical Architect [Interview]

Back in February, Viktor Charypar, Tech Director at Red badger explained the benefits of using a monorepo. For many teams, especially those without the resources or a highly developed and well-supported engineering culture, the idea of a monorepo might sound a little strange - following on from this piece, I spoke to Viktor to get a little bit more detail on the benefits of a monorepo and why engineering teams should seriously consider using them. But I didn't just speak to him about monorepos - I was also interested in how Red Badger builds a forward thinking engineering culture that can empower its clients, and how the team embraces continuous learning to ensure everyone is on top of the trends and tools that are going to be impacting digital transformation in the future. So, let's take a look at what Viktor had to say... Why monorepos now? Richard Gall: Why a monorepo now? If you’re dealing with multiple microservices doesn’t it make sense to separate source code? Viktor Charypar: It would seem to make sense, but microservices architecture actually introduces a new level of complexity that needs to be managed, which monorepos make much easier. The main issue is in dependencies between the services and the contracts they agree to exchange data. If one side of the contract changes in an incompatible way without the other side adapting to that change, the system no longer works. This problem grows with the number of services in the system and so it’s especially prominent in microservices architectures. Managing each service’s source code in a separate repository makes it more difficult to understand its ties to the rest of the system. This forces you to adopt some kind of external versioning scheme, such as semantic versioning, to express which revisions of services work together as things change. These versions are decided by engineers manually as changes are made according to a set of rules, and then the dependent services are updated to refer to the latest version of the service they consume, when they are changed to be compatible. This is time-consuming and error-prone. In a monorepo, all the components of the system are versioned together and changes can be made across the system. This not only means an external versioning scheme is not required, but it also makes it easier to test and enforce contracts between services. Monorepos really come to their own when they are coupled with a Continuous Integration system aware of the dependencies between components in the repo. Given a change made by a developer and the knowledge of the dependency “graph”, we can deterministically decide which system components can be affected by the change and therefore need to be retested. It is then up to owners of each service to do a level of testing of their dependencies to make sure their behaviour didn’t change significantly enough to break their own functionality. All this is automated and can be executed without human intervention. Humans just make changes to the software and express expectations on their dependencies in terms of contracts and tests. Read next: Mozilla’s updated policies will ban extensions with obfuscated code Digital transformation challenges RG: What common problems are clients coming to you with? VC: In general, our clients recognise they need help with their digital product capabilities, i.e. delivering interesting propositions to their customers as digital products, typically websites and mobile apps. In large enterprise companies, this ability is generally predicated on going through a digital transformation - adopting agile delivery methods, breaking down functional silos and working in cross-discipline, vertically aligned teams that can decide things quickly and adapt to how customers respond to their product offering. Our clients typically come to us with one of a few problems ranging from needing help with product strategy, i.e. what to offer their customers and how to find which of the many ideas have a market fit. Through knowing what to do but struggling to deliver it at pace, all the way to already having a digital product offering, but one which doesn’t perform as expected. Either from the perspective of customer behaviour (e.g. low conversion rate) or from a technical quality perspective, i.e. the website is unstable, struggles under high load, there are long outages, etc. While our strength is traditionally in fast product delivery and quality, we can help across the board, from product strategy to what we call empower and embed - demonstrating how to deliver digital products quickly, sustainably and with high quality, helping to build internal capability and then handing over to them. Essentially we want to help our clients build sustainable businesses. Read next: Linux forms Urban Computing Foundation: Set of open source tools to build autonomous vehicles and smart infrastructure Learning and assessing new software and tools RG: How do you stay on top of new tools? Do you have a learning culture at Red Badger? VC: We absolutely do, from simple day to day things like all engineers being encouraged to pair program to learn from each other or everyone in the company having a yearly training budget as one of the benefits, to doing things like a yearly internal mini conference called Tech Lab for all the engineers to get together and share latest learnings and general experience from projects. We have actually recently published a report which started with an activity at the last Tech Lab, which answers a lot of the questions above. It’s available on our website here (and we’ll also follow it with a series of events). We also run a few regular meetups in London, the biggest being the London React Meetup, which we’ve been hosting regularly for about four years. RG: How do you assess tools? VC: There are a few things we generally look at. The first is obviously experimenting with the tool to work out what it does and how. We’re in a privileged position of starting new projects, often greenfield ones, fairly regularly. We typically use about 80% of tools we know and trust and about 20% of new ones, which we want to try out “in anger” and learn about. We also look at who is behind these tools, which are generally open source, and whether there is momentum behind them and support from the community. Open source software typically goes through a period of rapid innovation and competition in a certain area and then, eventually, the community settles on a few options that work the best and fit the different problems people are trying to solve. The future of open source - is it sustainable? RG: How do you see the future of open source - is it sustainable on its current model? VC: That’s an interesting thing to think about! It seems like the open source model is widely misunderstood as software being built by dedicated developers in their free time. But in reality, most large, popular open source projects are backed by large software companies and people maintain them as their day job - for example, Linux, Kubernetes, React. Even the web standards are set by standard bodies comprised of professionals supported by the major browser vendors. I think the model with a sole maintainer working on something in their spare time doesn’t really work if their project gets very popular and the demand on their time grows. We all know how people tend to behave on the internet and software industry is no exception, so maintainers who do it as a hobby are at a pretty high risk of burning out. For the major open source projects, this seems to be more of an exception, as they are typically maintained by a team of people employed by a company invested in the project. The sponsor benefits from the community contributions and, if the project gets popular, from controlling the direction of a de facto standard and the community benefits from someone else doing the lion’s share of the work. I look at it as being similar to science, where different people publicly contribute to push the boundaries of knowledge, just because pooling resources makes more sense and doesn’t stop any individual contributor from profiting on the results. In that sense, I think it’s a pretty sustainable model and leads to better quality, more versatile software.

In the past few years, Agile software development has seen tremendous growth. There is a huge demand for software delivery solutions that are fast, yet flexible to numerous amendments. As a result, Continuous Integration (CI) and Continuous Delivery (CD) methodologies are gaining popularity. They are considered to be the cornerstones of DevOps and drive the possibilities of modern architectures like microservices and cloud native. Author’s Bio Nikhil Pathania, a DevOps practitioner at Siemens Gamesa Renewable Energy, started his career as an SCM engineer and later moved on to learn various tools and technologies in the fields of automation and DevOps. Throughout his career, Nikhil has promoted and implemented Continuous Integration and Continuous Delivery solutions across diverse IT projects. He is the author of Learning Continuous Integration with Jenkins. In this exclusive interview, Nikhil gives us a sneak peek into the trends and challenges of Continuous Integration in DevOps. Key Takeaways The main function of Continuous Integration is to provide feedback on integration issues. When practicing DevOps, a continuous learning attitude, sharp debugging skills, and an urge to improvise processes is needed Pipeline as a code is a way of describing a Continuous Integration pipeline in a pre-defined syntax One of the main reasons for Jenkin’s popularity is it’s growing support via plugins Making yourself familiar with a scripting language like Shell or Python will help you accomplish difficult tasks related to CI/CD Continuous Integration is built on Agile and requires a fair understanding of the 12 principles. Full Interview On the popularity of DevOps DevOps as a concept and culture is gaining a lot of traction these days. What is the reason for this rise in popularity? What role does Continuous Integration have to play in DevOps? To understand this, we need to look back at the history of software development. For a long period, the Waterfall model was the predominant software development methodology in practice. Later, when there was a sudden surge in the usage and development of software applications, the Waterfall model proved to be inefficient, thus giving rise to the Agile model. This new model proposed coding, building, testing, packaging, and releasing software in a quick and incremental fashion. As the Agile model gained momentum, more and more teams wanted to ship their applications faster and more frequently. This added a huge pressure on the release management process. To cope up with this pressure, engineers came up with new processes and techniques (collectively bundled as DevOps), such as the usage of improved branching strategies, Continuous Integration, Continuous Delivery, Automated environment provisioning, monitoring, and configuration. Continuous Integration involves continuous building and testing of your integrated code; it’s an integral part of DevOps, dealing with automated builds, testing, and more. Its core function is to provide a quick feedback on the integration issues. On your journey as a DevOps engineer You have been associated with DevOps for quite some time now and hold vast experience as a DevOps engineer and consultant. How and when did your journey start? Which tools did you master to help you with your day-to-day tasks? I started my career as a Software Configuration Engineer and was trained in SCM and IBM Rational Clearcase. After working as a Build and Release Engineer for a while, I turned towards new VCS tools such as Git, automation, and scripting. This is when I was introduced to Jenkins followed by a large number of other DevOps tools such as SonarQube, Artifactory, Chef, Teamcity, and more. It’s hard to spell out the list of tools that you are required to master since the list keeps increasing as the days pass by. There is always a new tool in the DevOps tool chain replacing the old one. A DevOps tool itself changes a lot in its usage and working over a period of time. A continuous learning attitude, sharp debugging skills, and an urge to improvise processes is what is needed, I’ll say. On the challenges of implementing Continuous Integration What are some of the common challenges faced by engineers in implementing Continuous Integration? Building the right mind-set in your organization: By this I mean preparing teams in your organisation to get Agile. Surprised! 50% of the time we spend at work is on migrating teams from old ways of working to the new ones. Implementing CI is one thing, while making the team, the project, the development process, and the release process ready for CI is another. Choosing the right VCS tool and CI tool: This is an important factor that will decide where your team will stand a few years down the line—rejoicing in the benefits of CI or shedding tears in distress. On how the book helps overcome these challenges How does your book 'Learning Continuous Integration with Jenkins' help DevOps professionals overcome the aforementioned challenges? This is why I have a whole chapter (Concepts of Continuous Integration) explaining how Continuous Integration came into existence and why projects need it. It also talks a little bit about the software development methodologies that gave rise to it. The whole book is based on implementing CI using Jenkins, Git, Artifactory, SonarQube, and more. About Pipeline as a Code Pipeline as a Code was a great introduction in Jenkins 2. How does it simplify Continuous Integration? Pipeline as a code is a way of describing your Continuous Integration pipeline in a pre-defined syntax. Since it’s in the form of code, it can be version-controlled along with your source code and there are endless possibilities of programming it, which is something you cannot get with GUI pipelines. On the future of Jenkins and competition Of late, tools such as TravisCI and CircleCI have got a lot of positive recognition. Do you foresee them going toe to toe with Jenkins in the near future? Over the past few years Jenkins has grown into a versatile CI/CD tool. What makes Jenkins interesting is its huge library of plugins that keeps growing. Whenever there is a new tool or technology in the software arena, you have a respective plugin in Jenkins for it. Jenkins is an open source tool backed by a large community of developers, which makes it ever-evolving. On the other hand, tools like TravisCI and CircleCI are cloud-based tools that are easy to start with, limited to CI in their functionality, and work with GitHub projects. They are gaining popularity mostly in teams and projects that are new. While it’s difficult to predict the future, what I can say for sure is that Jenkins will adapt to the ever-changing needs and demands of the software community. On key takeaways from the book Learning Continuous Integration with Jenkins Coming back to your book, what are the 3 key takeaways from it that readers will find to be particularly useful? In-depth coverage of the concepts of Continuous Integration. A step-by-step guide to implementing Continuous Integration, Continuous Delivery with Jenkins 2 using all the new features. A practical usage guide to Jenkins's future, the Blue Ocean. On the learning path for readers Finally, what learning path would you recommend for someone who wants to start practicing DevOps and, specifically, Continuous Integration? What are the tools one must learn? Are there any specific certifications to take in order to form a solid resume? To begin with, I would recommend learning a VCS tool (say Git), a CI/CD tool (Jenkins), a configuration management tool (Chef or Puppet, for example), a static code analysis tool, a cloud tool like AWS or Digital Ocean, and an artifactory management tool (say Artifactory). Learn Docker. Build a solid foundation in the Build, Release and Deployment processes. Learn lots of scripting languages (Python, Ruby, Groovy, Perl, PowerShell, and Shell to name a few), because the real nasty tasks are always accomplished by scripts. A good knowhow of the software development process and methodologies (Agile) is always nice to have. Linux and Windows administration will always come in handy. And above all, a continuous learning attitude, an urge to improvise the processes, and sharp debugging skills is what is needed. If you enjoyed reading this interview, check out Nikhil’s latest edition Learning Continuous Integration with Jenkins. Top 7 DevOps Tools in 2018 Everything you need to know about Jenkins X 5 things to remember when implementing DevOps

Augmented and Virtual Reality is taking on the world, one AR app at a time. Almost all tech giants have their own AR platforms, whether it be Google’s ARCore, to Apple’s ARKit, to Snapchat’s Lens Studio. Not just that, there are now various AR galleries and exhibits focused solely on AR, where designers and artists showcase their artistic talents combined with rich augmented reality experiences. While searching for designers and artists working the augmented reality space, we stumbled upon Marc-O-Matic aka Marco Ryan. We were fascinated by his artwork and wanted to interview him right away.   [embed]https://vimeo.com/219045989[/embed] As his website bio says, “Marco is a multidisciplinary Artist, Animator, Director, Storyteller and Technologist working across Augmented and Virtual Reality technologies.” He shared with us his experiences working with Augmented Reality and told us about his creation process, his current projects, tips and tricks for a budding artist venturing into the AR space and his views on merging technology and art. Key Takeaways A well-rounded creative is someone who could do everything from creating the art, the associated stories as well as execute the technical aspects of the work to make it come to life. The future belongs to these creative types who love to learn and experiment every day. An artist must consider three aspects before taking on a project. First, how to tell their story in a more engaging and interesting manner. Second, how to combine different skill sets together and third the message their work conveys. Augmented Reality has added a new level of depth to the art experience not just for artists but also for viewers. Everyone has a smartphone these days and with AR you can add many added elements to an art piece. You can add sound, motion and 3D elements to the experience, which affect more of your senses. It is easy for a beginner artist to get started with creating Augmented reality art. You need to start with learning a basic language (mostly C# or Javascript) and then you can learn more as you explore. Tools and platforms such as Adobe After Effects, Maya and Blend, are good for building shaders, materials or effects. Unity 3D is a popular choice for implementing the AR functionality, and over 91% of HoloLens experiences are made with Unity. It supports a large number of devices and also has a large community base. Unity offers highly optimized rendering pipeline and rapid iteration capabilities to create realist AR VR experiences. AI and Machine Learning are at the forefront of innovating AR/VR. Machine learning algorithms can be used in character modeling. They can map your facial movements directly to a 3D character’s face. AI can make a character emotionally react based on the tone of the audio and also automate tasks allowing an artist to focus solely on the creative aspects. Full Interview How did you get started in AR. How did you journey from being an artist to an AR artist began? I started experimenting with Immersive AR/VR tech about 2-3 years ago? At the time buzzwords like virtual and augmented reality started trending and it had me curious to see how I could implement my own existing skills into this area at the time. Prior to getting into AR and VR I came from a largely self-taught background in Art & Illustration which then evolved into exploring film and animation and then finally led to game design and programming. Exploring all these areas allowed me to become a well-rounded creative, meaning I could do everything from creating the art, the associated stories as well as execute the technical aspects of the work to make it come to life. That’s quite impressive and fascinating to know! What programming language did you learn first and why? What was your approach to learning the language? Do you also dabble in other languages? How can artists overcome the fear of learning technology? Working with the game engine Unity 3D to create my VR and AR Experiences, the engine allows you to program in either C# or Javascript. C# was the the most prevalent language of the two and for me I found it easier to pick up. As a visual learner, I initially came to understand the language through node based programming tools like Playmaker and Uscript. These plug-ins for Unity are great for beginners as they allow you to visually program behaviors and functionality into your experience by creating interconnected node trees or maps which then generates the code. I’m familiar with this form of logic building as other programs I use, such as Adobe After Effects, Maya and Blend, use similar systems for building shaders, materials or effects. Through node based programming or visual scripting, you can take a look under the hood and understand the syntax iteratively. It’s like understanding a language through reverse-engineering. By seeing the code being generated based on the node maps you create you could quickly understand how the language is structured. I try to think of learning new things as adding skills to your arsenal and the more skills you possess the greater you can expand on your ideas. I don’t think it’s so much a fear of learning new technology. I think it’s more a question of ‘Will learning this new technology be worth my time and benefit my current practice?’ The beautiful age we live in makes it so much easier to learn new skills online. So there’s so much support already available for anyone wanting to explore game design technologies in their creative practice. What tools do you use in the process and why those tools? What is the actual process you follow? What are the various steps? The Augmented Reality Art I created has two parts to them: Creating the Art Everything is drawn on paper first using your typical office ballpoint pens, inkwash and sometimes watercolours or gouache. I prefer creating my work that way. To me keeping that 'hand-crafted' style and aesthetic has its charm when bringing it to life through Augmented Reality. I think it's really important to demonstrate the importance of other traditional mediums, techniques and disciplines when working with immersive technologies as they're still very valid in creating engaging content. I see a lot of AR experiences that have a lack of charm and feel somewhat 'gamey' which is why I want to continue integrating more raw-looking aesthetics in this area. Augmented Representation & Implementation Once the art is planned and created, I then scan it and start splicing it up digitally. From the preserved textures and linework I then create a 3D representation of the artwork through 3D modeling and animation software called Blender. It's one of many 3D authoring tools out there. It not only packs a significant number of features, it's also free which makes it ideal for beginners and students. Once the 3D representation of the work is created it's then imported into a game engine called Unity3D where I implement the AR functionality. Unity 3D is a widely used game engine. It's one of many engines out there but what makes it great is its support to deploy to all manners of devices. It also has a large community base behind it should you need help. How long does a typical project take? Do you work by yourself or with a team? How long do you work? On average an Augmented Artwork may take anywhere from 3 to 4 weeks to create, which includes the Art, Animation, Modeling and AR Implementation. When I first started out it'd take much longer but overtime I've streamlined my own processes to get things done faster. My alias Marc-O-Matic tends to be mistaken as a team but I'm actually just one person. I much prefer being able to create and direct everything myself. What is your advice to a budding artist who is interested in learning tech to produce art? From my experience, don't confine yourself to one specific medium and try practising a different skill or technique everyday. As artists we should be constantly evolving not just our technical skills but also our thought processes to push the boundaries on what's achievable. 'How can I tell my story in a more engaging and interesting manner?' 'What can I create if I combine these different skill sets together?" "How do I bring an end to systematic racism and white privilege?' etc. The more knowledge you have the greater you can push the boundaries of your creative expression. How do you support yourself financially? As in do you also sell your art pieces or make art for clients etc.? Where do you sell stuff, and meet people? I work under my own Artist Entity 'Marc-O-Matic.' I much prefer working independently than within larger studios or agencies. I juggle a balance between commercial work and personal projects. There's generally a high demand for Augmented/Virtual Reality experiences and I'm really lucky and grateful that clients want content in my particular style. I work with a variety of organisations generally consulting, providing creative direction and in most cases building the experiences too. Aside from the commercial work, I'm currently touring my own Augmented Reality Art/Storytelling collection called 'Moving Marvels' where audiences get to see my illustrated works come to life right in front of them. It's also how I sell various limited edition prints of my augmented artworks. My collection is exhibited at tech/innovation conferences, symposiums, galleries and even at universities/educational institutes. It's a great way to make connections and demonstrate what immersive technologies can do in a creative capacity. No interview would be complete without a discussion on AI and machine learning. As a technophile artist, are you excited about merging AI and art in new ways? What are some of your hopes and fears about this new tech? Do you plan to try machine learning anytime soon? It’s like a double edged sword with + 5 to Creativity. Technology can enhance our creative abilities and processes in a number of ways. At the same time it can also make some of us lazy because we can become so reliant on it. Like any new technology that aims to assist in the creative process, there’s always the fear that technology will make creatives lazy or will replace professions altogether. In some ways technology has done this but from saying that it has also created new job opportunities and abilities for artists. In areas like character animation for example the assistance of machine learning algorithms means creatives can worry less about the laborious physical processes of rigging and complex animation and focus more on the storytelling. For example, we can significantly reduce production times in the areas of facial rigging through facial recognition. Through learning the behaviour and structure of your own face, machine learning algorithms can map your facial movements directly to a 3D character’s face. By simply recording your own facial movements and gestures, you’ve created an entire impression map for your 3D character to use. What’s also crazy is, on top of that, by running a voice over track on that 3D character, you can also train it to play it out to sync with the voice AS well as have the entire face emotionally react based on the tone of the audio. It’s game-changing but also terrifying stuff as this sort of technology can be used to create highly realistic fake impressions of real people. As also an animator, I’ve started experimenting with this technology for my own VR animations. What would take me animator hours or even days to animate a talking 3D character can now take mere minutes. Author Bio Marc-O-Matic is the moniker of Marco Matic Ryan. He is a multidisciplinary Artist, Animator, Director, Storyteller and Technologist working across Augmented and Virtual Reality technologies. He is based in Victoria, Australia. https://player.vimeo.com/video/125218939 Unity plugins for augmented reality application development. Google ARCore is pushing immersive computing forward. There’s another player in the advertising game: augmented reality.

We've been talking a lot about observability on the Packt Hub over the last few months. Back in March we spoke to Honeycomb CEO Charity Majors who told us why observability is so important and why it can be so challenging for engineering teams to implement. It's clear it's a big topic with plenty of perspectives - but one that could have a ripple effect across the software industry. To get a further perspective on the topic, we spoke to Yuri Shkuro, who's an engineer at Uber and author of Mastering Distributed Tracing (which was published in February) to talk about how distributed tracing can help engineers build more observable systems. Yuri spoke in detail in the podcast about the value of observability in the context of complex distributed systems, as well as some of the challenges in implementing distributed tracing. As one of the creators of Jaeger, an open source tool built specifically for distributed tracing, he's well-placed to comment on how the ecosystem is evolving and how organizations can start thinking more seriously about observability. Read an extract from Yuri's book here. The episode covers: The difference between monitoring and observability Some of the misconceptions around distributed tracing Who can benefit from distributed tracing - from DevOps to SREs Practical advice for getting started with distributed tracing Listen on SoundCloud: https://soundcloud.com/packt-podcasts/if-youre-on-call-you-need-observability-tools-uber-engineer-yuri-shkuro-on-distributed-tracing “Tracing is conceptually a white box instrumentation technique. You cannot do tracing in an application by purely observing it from the outside, because that feature of context propagation is simply not possible - if you have 10 incoming requests into an application concurrently, and it does 100 outbound requests then how do you know which ones correlate to the incoming requests? That’s what context propagation allows us to achieve, it allows us to establish causality within events.”

Robotics competitions have evolved from the time I participated in themduring my college days. Thanks to microboards such as the Raspberry Pi, it’s much more accessible – it could quite literally be described as ‘child’s play’. Mike Horne, the organizer of PiWars and co-organiser of CamJam, alongside his friend Tim Richardson, has taken his close connection to the Raspberry Pi project to inspire tech fans and hackers of all ages. PiWars is unique- it’s not just about knocking over your combatant’s robot, or following the terrain, it’s about the entire learning and development process.I was lucky enough to get to talk to Michael about PiWars, robotics and the immense popularity of Raspberry Pi. What kick-started PiWars and CamJam? CamJam started because I couldn’t understand why there wasn’t a Raspberry Jam in the Pi’s home town. There had been a couple of Cambridge Jams but they stopped quite early. I resurrected it by starting small (with just 30 people in one room) and it’s grown from there. Tim Richardson came onboard as co-planner after my second Jam and encouraged me to get a larger venue where we could run workshops as well as talks. We now work hand-in-hand to make the events as good as possible. PiWars was Tim’s idea. We both fondly remember the television programme ‘Robot Wars’ and he wondered whether we couldn’t do something similar, but with challenges instead of ‘fights’. And it all went from there. What sets PiWars’ apart from other robotics challenge? What is your vision 2020? What sets it apart first of all is that it is ‘non-destructive’. Although we used the name PiWars, no robots are intentionally damaged. We believe this is key to the enjoyment of the competitors as it means their good work isn’t destroyed. Apart from that, the use of the Raspberry Pi makes it unique – each robot must have a Pi at its core. When was the last time you competed in a robotics challenge or created a robot? I’ve personally never competed in a robotics challenge – the opportunities just haven’t been there. I did actually go and see Robot Wars being filmed once, which was exciting! I created a robot about two weeks ago whilst preparing for the launch of CamJamEduKit 3. It’s a robotics kit that’s available from The Pi Hut for £17 and contains everything you need to build a robot except batteries and a chassis (although the box it comes in makes a really good chassis!) You guys did a great job in organising thePiWars, CamJam and RaspberryPi birthday party. What are the challenges you faced, and ideas you came up with? Mostly the challenge is two-fold: 1. Persuading people to come and do talks, help with workshops and give general help on the day. 2. Logistics – it takes a lot of paperwork, spreadsheets and checklists to run an event on this scale. It’s always about working out what scale of event you want to run. CamJam is pretty steady now as we’ve got a structure. Pi Wars, being the second year, has expanded and changed organically. For The Big Birthday Weekend we came up with the idea of having two lots of workshops running at the same time as two lots of talks. Ideas-wise, we use beer to get things kicked off J. Tim’s great with coming up with new ways to make the events better. The Marketplace area was his idea. Show-and-Tell was mine. It’s a great collaboration. Not everyone could participate/physically be present in such competitions, do you think hosting a virtual competition though skype can be possible? We did consider it last year, actually! Someone from Australia wanted to send his robot via freight and control it over the Internet. We didn’t think that would work due to technical limitations. The main problem with holding a virtual competition is: where do you put the challenge courses? Do you have them in one location and then have robots remote-controlled or do you have the competitors recreate the courses in their location somehow? Then, how do you deal with the video streaming to spectators? How can robotics be taught in an effective manner with limited resources? How do you think Packt is contributing? The main barrier to entry with robotics is not the cost of equipment, although that does play a part. The main barrier is lack of material to support the learning. It’s one of the things we’ve concentrated on with the EduKits – good, solid resource worksheets. Packt have been doing a great job by publishing several books which contain at least an element of robotics, and sometimes by devoting entire publications to the subject. You may have seen some of our books mentioned in the MagPi, but do you use books to learn about Raspberry Pi yourself? I do. I’ve learned a lot of the basics from Adventures in Raspberry Pi (by Carrie Anne Philbin) and use Alex Bradbury and Ben Everard’s Python book as a reference. I’ve also looked at several Packt publications for inspiration for Raspberry Pi projects. Complete these sentences… Robotics challenge is not about smashing, it is… about learning how to give your robot the skills it needs. PiZero is…incredibly cute and brings a lot of hope for the future of embedded and IoT Raspberry Pi projects. Code quality, build quality, aesthetics and blogging is not just to rank the robot, it helps to… focus the minds of the competitors in building the best robot they can. My favourite Raspberry Pi project… at the moment is probably the S.H.I.E.L.D.-inspired ‘den’ I blogged about recently. Long-term, I really like Dave Akerman’s work on getting pictures from near-space using high-altitude balloons with a Pi and camera module. My words of wisdom for young hacker are… “Don’t be limited by anything, not even your imagination. Push yourself to come up with new and interesting things to do, and don’t be afraid to take someone else’s idea and run with it.” This or That- Tea or coffee? Coffee Linux or Python? Both GeekGurl or RaspberryPi Guy? They’re both friends – I’m not landing myself in hot water for that one! Terminators or Transformers? Transformers, but the ones from the 1980s, not Michael Bay’s questionable version! Raspberry Pi or BBC micro:bit? Raspberry Pi, all the way. The micro:bit just doesn’t do enough to really stretch youngsters. We’re big fans of DIY tech at Packt – like Raspberry Pi, we’re passionate about innovation and new ideas. That’s why from Monday 7th to Sunday 13th December we’re celebrating Maker Week. We’re giving away free eBooks on some of the world’s most exciting microcomputers – including Raspberry Pi and Arduino – and offering 50% off some of our latest guides to creative tech.

Data Science has popularly been dubbed as the sexiest job of the 21st century. So much so that everyone wants to become a data scientist. But what do you need to get started with data science? Do you need to have a degree in statistics? Why is having sound knowledge of statistics so important to be a good data scientist? We seek answers to these questions and look at data science through a statistical lens, in an interesting conversation with James D. Miller. [author title="James D. Miller"]James is an IBM certified expert and a creative innovator. He has over 35 years of experience in applications and system design & development across multiple platforms and technologies. Jim has also been responsible for managing and directing multiple resources in various management roles including project and team leader, lead developer and applications development director. He is the author or several popular books such as Big Data Visualization, Learning IBM Watson Analytics, Mastering Splunk, and many more. In addition, Jim has written a number of whitepapers and continues to write on a number of relevant topics based upon his personal experiences and industry best practices.[/author] In this interview, we look at some of the key challenges faced by many while transitioning from a data developer role to a data scientist. Jim talks about his new book, Statistics for Data Science and discusses how statistics plays a key role when it comes to finding unique, actionable insights from data in order to make crucial business decisions. Key Takeaways - Statistics for Data Science Data science attempts to uncover the hidden context of data by going beyond answering generic questions such as ‘what is happening’, to tackling questions such as ‘what should be done next’. Statistics for data science cultivates 'structured thinking' in one. For most data developers who are transitioning to the role of data scientist, the biggest challenge often comes in calibrating their thought process - from being data design-driven to more insight-driven Having a sound knowledge of statistics differentiates good data scientists from mediocre ones - it helps them accurately identify patterns in data that can potentially cause changes in outcomes Statistics for Data Science attempts to bridge the learning gap between database development and data science by implementing the statistical concepts and methodologies in R to build intuitive and accurate data models. These methodologies and their implementations are easily transferable to other popular programming languages such as Python. While many data science tasks are being automated these days using different tools and platforms, the statistical concepts and methodologies will continue to form their backbone. Investing in statistics for data science is worth every penny! Full Interview Everyone wants to learn data science today as it is one of the most in-demand skills out there. In order to be a good data scientist, having a strong foundation in statistics has become a necessity. Why do you think is this the case? What importance does statistics have in data science? With Statistics, it has always been about "explaining" (data). With data science, the objective is going beyond questions such as "what happened?" and the "what is happening?" to try to determine "what should be done next?". Understanding the fundamentals of statistics allows one to apply "structured thinking" to interpret knowledge and insights sourced from statistics. You are a seasoned professional in the field of Data Science with over 30 years of experience. We would like to know how your journey in Data Science began, and what changes you have observed in this domain over the 3 decades. I have been fortunate to have had a career that has traversed many platforms and technological trends (in fact over 37 years of diversified projects). Starting as a business applications and database developer, I have almost always worked for the office of finance. Typically, these experiences started with the collection - and then management of - data to be able to report results or assess performance. Over time, the industry has evolved and this work as becoming a “commodity” – with many mature tool options available and plenty of seasoned professionals available to perform the work. Businesses have now become keen to “do something more” with their data assets and are looking to move into the world of data science. The world before us offers enormous opportunities for those not only with a statistical background but someone with a business background that understands and can apply the statistical data sciences to identify new opportunities or competitive advantages. What are the key challenges involved in the transition from being a data developer to becoming a data scientist? How does the knowledge of statistics affect this transition? Does one need a degree in statistics before jumping into Data Science? Someone who has been working actively with data already has a “head start” in that they have experience with managing and manipulating data and data sources. They would also most likely have programming experience and possess the ability to apply logic to data. The challenge will be to “retool” their thinking from data developer to data scientist – for example, going from data querying to data mining. Happily, there is much that the data developer “already knows” about data science and my book Statistics for Data Science attempts to “point out” the skills and experiences that the data developer will recognize as the same or at least have significant similarities. You will find that the field of data science is still evolving and the definition of “data scientist” depends upon the industry, project or organization you are referring to. This means that there are many roles that may involve data science with each having perhaps quite different prerequisites (such as a statistical degree). You have authored a lot of books such as Big Data Visualization, Learning IBM Watson Analytics, etc. with the latest being Statistics for Data Science. Please tell us something about your latest book. The latest book, “Statistics for Data Science”, looks to point out the synergies between a data developer and data scientist and hopes to evolve the data developers thinking “beyond database structures”, but also introduces key concepts and terminologies such as probability, statistical inference, model fitting, classification, regression and more, that can be used to journey into statistics and data science. How is statistics used when it comes to cleaning and pre-processing the data? How does it help the analysis? What other tasks can these statistical techniques be used for? Simple examples of the use of statistics when cleaning and/or pre-processing of data (by a data developer) include data-typing, Min/Max limitation, addressing missing values and so on. A really good opportunity for the use of statistics in data or database development is while modeling data to design appropriate storage structures.  Using statistics in data development applies a methodical, structured approach to the process. The use of statistics can be a competitive advantage to any data development project. In the book, for practical purposes, you have shown the implementation of the different statistical techniques using the popular R programming language. Why do you think R is favored by the statisticians so much? What advantages does it offer? R is a powerful, feature-rich, extendable free language with many, many easy to use packages free for download. In addition, R has “a history” within the data science industry. R is also quite easy to learn and be productive with quickly. It also includes many graphics and other abilities “built-in”. Do you foresee a change in the way statistics for data science is used in the near future? In other words, will the dependency on statistical techniques for performing different data science tasks reduce? Statistics will continue to be important to data science. I do see more “automation” of more and more data science tasks through the availability of “off the shelf” packages that can be downloaded and installed and used. Also, the more popular tools will continue to incorporate statistical functions over time. This will allow for the main-streaming of statistics and data science into even more areas of life. The key will be for the user to have an understanding of the key statistical concepts and uses. What advice would you like to give to - 1 Those transitioning from the developer to the data scientist role, and 2. Absolute beginners, who want to take up statistics and data science as a career option? Buy my book! But seriously, keep reading and researching. Expose yourself to as much statistics and data science use cases and projects a possible. Most importantly, as you read about the topic, look for similarities between what you do today and what you are reading about. How does it relate? Always look for opportunities to use something that is new to you to do something you do routinely today. Your book 'Statistics for Data Science' highlights different statistical techniques for data analysis and finding unique insights from data. What are the three key takeaways for the readers, from this book? Again, I see (and point out in the book) key synergies between data or database development and data science. I would urge the reader – or anyone looking to move from data developer to data scientist - to learn through these and perhaps additional examples he or she may be able to find and leverage on their own. Using this technique, one can perhaps navigate laterally, rather than losing the time it would take to “start over” at the beginning (or bottom?) of the data science learning curve. Additionally, I would suggest to the reader that time taken to get acquainted with the R programs and the logic used for statistical computations (this book should be a good start) is time well spent.  

Cloud-native applications are an assembly of independent services used to build new applications, optimize existing ones, and connect them in such a way that the applications can skillfully deliver the desired result. More specifically, they are employed to build scalable and fault-tolerant applications in public, private, or hybrid clouds.  Launched in March this year, Quarkus, a new Kubernetes-native framework launched its first stable version, Quarkus 1.0 last month. Quarkus allows Java developers to combine the power of containers, microservices, and cloud-native to build reliable applications. To get a more clear understanding of Cloud-Native Applications with Java and Quarkus, we interviewed Francesco Marchioni, a Red Hat Certified JBoss Administrator (RHCJA) and Sun Certified Enterprise Architect (SCEA) working at Red Hat. Francesco is the author of the book ‘Hands-On Cloud-Native Applications with Java and Quarkus’.  Francesco on Quarkus 1.0 and how Quarkus is bringing Java into the modern microservices and serverless modes of developing Quarkus is coming up with its first stable version Quarkus 1.0 at the end of this month. It is expected to have features like a new reactive core based on Vert.x, a non-blocking security layer, and a new Quarkus ecosystem called ‘universe’. What are you most excited about in Quarkus 1.0? What are your favorite features in Quarkus? One of my favorite features of Quarkus is the reactive core ecosystem which supports both reactive and imperative programming models, letting Quarkus handle the execution model switch for you. This is one of the biggest gains you will enjoy when moving from a monolithic core, which is inherently based on synchronous executions, to a reactive environment that follows events and not just a loop of instructions. I also consider of immense value that the foundation of Quarkus API is a well-known set of APIs that I was already skilled with, therefore I could ramp up and write a book about it in less than one year! How does the Quarkus Java framework compare with Spring? How do you think the Spring API compatibility in Quarkus 1.0 will help developers? Both Quarkus and Spring boot offer a powerful stack of technologies and tools to build Java applications. In general terms, Quarkus inherits its core features from the Java EE, with CDI and JAX-RS being the most evident example. On the other hand, Spring boot follows an alternative modular architecture based on the Spring core. In terms of Microservices, they also differ as Quarkus leverages the Microprofile API while Spring Boot relies on Spring Boot Actuator and Netflix Hystrix. Besides the different stacks, Quarkus has some unique features available out of the box such as Build time class initialization, Kubernetes resources generation and GraalVM native images support. Although there are no official benchmarks, in the typical case of a REST Service built with Quarkus, you can observe an RSS memory reduction to half and a 5x increase in boot speed. In terms of compatibility, it's worth mentioning that, while users are encouraged to use CDI annotations for your applications, Quarkus provides a compatibility layer for Spring dependency injection (e.g. @Autowired) in the form of the spring-di extension. Quarkus is tailored for GraalVM and crafted by best-of-breed Java libraries and standards. How do you think Quarkus brings Java into the modern microservices and serverless modes of developing? Also, why do you think Java continues to be a top programming language for back-end enterprise developers? Although native code execution, in combination with GraalVM, Quarkus is an amazing opportunity for Java. I mean I wouldn't say Quarkus is just native centric as it immediately buys to Java developers an RSS memory reduction to about half, an increase in boot speed, top Garbage Collector performance, plus a set of libraries that are tailored for the JDK. This makes Java a first-class citizen in the microservices ecosystem and I bet it will continue to be one of the top programming languages still for many years. On how his book will benefit Java developers and architects In your book “Hands-On Cloud-Native Applications with Java and Quarkus” you have demonstrated advanced application development techniques such as Reactive Programming, Message Streaming, Advanced configuration hacks. Apart from these, what are the other techniques that can be used for managing advanced application development in Quarkus? Also, apart from the use cases in your book, what other areas/domains can you use Quarkus? In terms of configuration, a whole chapter of the book explores the advanced configuration options which are derived from the MicroProfile config API and the Applications’ profile management, which is a convenient way to shift the configuration options from one environment to another- think for example how easy can be with Quarkus to switch from a Production DB to a Development or Test Database. Besides the use cases discussed in the book, I’d say Quarkus is rather polyvalent, based on the number of extensions that are already available. For example, you can easily extend the example provided in the last chapter, which is about Streaming Data, with advanced transformation patterns and routes provided by the camel extension, thus leveraging the most common integration scenarios. What does your book aim to share with readers? Who will benefit the most from your book? How will your book help Java developers and architects in understanding the microservice architecture? This book is a log of my journey through the Quarkus Land which started exactly one year ago, at its very first internal preview by our engineers. Therefore my first aim is to ignite the same passion to the readers, whatever is their "maturity level" in the IT. I believe developers and architects from the Java Enterprise trenches will enjoy the fastest path to learning Quarkus as many extensions are pretty much the same they have been using for years. Nevertheless, I believe any young developer with a passion for learning can quickly get on board and become proficient with Quarkus by the end of this book. One advantage of younger developers over seasoned ones, like me, is that it will be easier for them to start thinking in terms of services instead of building up monolithic giant applications like we used to do for years. Although microservices patterns are not the main focus of this book, a lot of work has been done to demonstrate how to connect services and not just how to build them up. On how Red Hat uses Quarkus in its products and service Red Hat is already using Quarkus in their products and services. How is it helping Red Hat in increasing the efficiency of your Cloud-Native applications? To be precise, Quarkus is not yet a Red Hat supported Product, but it has already reached an important milestone with the release Quarkus 1.0 final, so it will definitely be included in the list of our supported products, according to our internal productization road-map. That being said, Red Hat is working in increasing the efficiency of your Cloud-Native applications in several ways through a combination of practices, technologies, processes that can be summarized in the following steps that will eventually lead to cloud-native application success: Evolve a DevOps culture and practices to embrace new technology through tighter collaboration. Speed up existing, monolithic applications with simple migration processes that will eventually lead to microservices or mini services. Use ready-to-use developer tools such as application services, to speed up the development of business logic. Openshift tools (web and CLI) is an example of it. Choose the right tool for the right application by using a container-based application platform that supports a large mix of frameworks, languages, and architectures. Provide self-service, on-demand infrastructure for developers using containers and container orchestration technology to simplify access to the underlying infrastructure, give control and visibility to IT operations, and provide application lifecycle management across environments. Automate IT to accelerate application delivery using clear service requirements definition, self-service catalogs that empower users (such as the Container catalog) and metering, monitoring of runtime processes. Implement continuous delivery and advanced deployment techniques to accelerate the delivery of your cloud-native applications. Evolve your applications into a modular architecture by choosing a design that fits your specific needs, such as microservices, a monolith-first approach, or mini services. On Quarkus’ cloud-native security and its competitors Cloud-native applications provide customers with a better time-to-market strategy and also allows them to build, more robust, resilient, scalable, and cost-effective applications. However, they also come with a big risk of potential security breaches. What is your take on cloud-native security for cloud-native applications? Also, what are your thoughts on future-proofing cloud applications? Traditionally, IT security was focused on hardening and the datacenter perimeter—but today, with Cloud applications, that perimeter is fading out. Public and hybrid clouds are shifting responsibility for security and regulatory compliance across the vendors. The adoption of containers at scale requires the adoption of new methods of analyzing, securing, and updating the delivery of applications. As a result, static security policies don’t scale well for containers in the enterprise but need to move to a new concept of security called "continuous container security". This includes some key aspects such as securing the container pipeline and the application, securing the container deployment environment(s) and infrastructure, integrating with enterprise security tools and meeting or enhancing existing security policies. About future-proofing of cloud applications, I believe proper planning and diligence can ensure that a company’s cloud investments withstand future change or become future-proof. It needs to be understood that new generation applications (such as apps for social, gaming and generally mobile apps) have different requirements and generate different workloads. This new generation of applications requires a substantial amount of dynamic scaling and elasticity that would be quite expensive or impossible to achieve with traditional architectures based on old data centers and bare-metal machines. Micronaut and Helidon, the other two frameworks that support GraalVM native images and target cloud-native microservices are often compared to Quarkus. In what aspects are they similar? And in what ways is Quarkus better than and/or different from the other two?   Although it is challenging to compare a set of cutting edge frameworks as some factors might vary in a middle/long term perspective, in general terms I'd say that Quarkus provides the highest level of flexibility especially if you want to combine reactive programming model with the imperative programming model. Also, Quarkus builds on the top of well-known APIs such as CDI, JAX-RS, and Microprofile API, and uses the standard "javax" namespaces to access them. Hence, the transition from the former Enterprise application is quite smooth compared with competitive products. Micronaut too has some interesting features such as support for multiple programming languages (Java, Kotlin, and Groovy the latter being exclusive of Micronaut) and a powerful Command Line Interface (CLI) to generate projects. (A CLI is not yet available in Quarkus, although there are plans to include it in the upcoming versions of it). On the other hand, Helidon is the less polyglot alternative (supports only Java right now) yet, it features a clean and simple approach to Container by providing a self-contained Dockerfile that can be built by simply calling docker build, not requiring anything locally (except the Docker tool of course). Also, the fact that Helidon plays well with GraalVM should be acknowledged as they are both official Oracle products. So, although for new projects the decision is often a matter of personal preferences and individual skills in your team, I'd say that Quarkus leverages existing Java Enterprise experience for faster results. If you want to become an expert in building Cloud-Native applications with Java and Quarkus, learn the end-to-end development guide presented in the book “Hands-On Cloud-Native Applications with Java and Quarkus”. This book will also help you in understanding a wider range of distributed application architectures to use a full-stack framework and give you a headsup on the new features in Quarkus 1.0. About the author Francesco Marchioni is a Red Hat Certified JBoss Administrator (RHCJA) and Sun Certified Enterprise Architect (SCEA) working at Red Hat in Rome, Italy. He started learning Java in 1997, and since then he has followed all the newest application program interfaces released by Sun. In 2000, he joined the JBoss community, when the application server was running the 2.X release. He has spent years as a software consultant, where he has enabled many successful software migrations from vendor platforms to open source products, such as JBoss AS, fulfilling the tight budget requirements necessitated by the current economy. Francesco also manages a blog on 'WildFly Application Server, Openshift, JBoss Projects and Enterprise Applications' focused on Java and JBoss technologies. You can reach him on Twitter and LinkedIn. RedHat’s Quarkus announces plans for Quarkus 1.0, releases its rc1  How Quarkus brings Java into the modern world of enterprise tech Introducing ‘Quarkus’, a Kubernetes native Java framework for GraalVM & OpenJDK HotSpot OpenJDK Project Valhalla’s head shares how they plan to enhance the Java language and JVM with value types, and more Snyk’s JavaScript frameworks security report 2019 shares the state of security for React, Angular, and other frontend projects

A lot of people rave about TensorFlow and Theano, but there are is one complaint you hear fairly regularly: that they can be a little challenging to use if you're directly building deep learning models. That’s where Keras comes to the rescue. It's a high-level deep learning library written in Python that can be used as a wrapper on top of TensorFlow or Theano, to simplify the model training process and to make the models more efficient. Sujit Pal is Technology Research Director at Elsevier Labs. He has been working with Keras for some time. He is an expert in Semantic Search, Natural Language Processing and Machine Learning. He's also the co-author of Deep Learning with Keras, which is why we spoke to him about why you should use start using Keras (he's very convincing). 5 reasons you should start using Keras Keras is easy to get started with if you’ve worked with Python before and have some basic knowledge of neural networks. It works on top of Theano and TensorFlow seamlessly to create efficient deep learning models. It offers just the right amount of abstraction - allowing you to focus on the problem at hand rather than worry about the complexity of using the framework. It is a handy tool to use if you’re looking to build models related to Computer Vision or Natural Language Processing. Keras is a very expressive framework that allows for rapid prototyping of models. Why I started using Keras Packt: Why did you start using using Keras? Sujit Pal: My first deep learning toolkit was actually Caffe, then TensorFlow, both for work related projects. I learned Keras for a personal project and I was impressed by the Goldilocks (i.e. just right) quality of the abstraction. Thinking at the layer level was far more convenient than having to think in terms of matrix multiplication that TensorFlow makes you do, and at the same time I liked the control I got from using a programming language (Python) as opposed to using JSON in Caffe. I've used Keras for multiple projects now. Packt: How has this experience been different from other frameworks and tools? What problems does it solve exclusively? Sujit: I think Keras has the right combination of simplicity and power. In addition, it allows you to run against either TensorFlow or Theano backends. I understand that it is being extended to support two other backends - CNTK and MXNet. The documentation on the Keras site is extremely good and the API itself (both the Sequential and Functional ones) are very intuitive. I personally took to it like a fish to water, and I have heard from quite a few other people that their experiences were very similar. What you need to know to start using Keras Packt: What are the prerequisites to learning Keras? And what aspects are tricky to learn? Sujit: I think you need to know some basic Python and have some idea about Neural Networks. I started with Neural Networks from the Google/edX course taught by Vincent Van Houke. It’s pretty basic (and taught using TensorFlow) but you can start building networks with Keras even with that kind of basic background. Also, if you have used numpy or scikit-learn, some of the API is easier to pick up because of the similarities. I think the one aspect I have had a few problems with is building custom layers. While there is some documentation that is just enough to get you started, I think Keras would be usable in many more situations if the documentation for the custom layers was better, maybe more in line with the rest of Keras. Things like how to signal that a layer supports masking or multiple tensors, debugging layers, etc. Packt: Why do you use Keras in your day-to-day programming and data science tasks? Sujit: I have spent most of last year working with Image classification and similarity, and I've used Keras to build most of my more recent models. This year I am hoping to do some work with NLP as it relates to images, such as generating image captions, etc. On the personal projects side, I have used Keras for building question answering and disease prediction models, both with data from Kaggle competitions. How Keras could be improved Packt: As a developer, what do you think are the areas of development for Keras as a library? Where do you struggle the most? Sujit: As I mentioned before, the Keras API is quite comprehensive and most of the time Keras is all you need to build networks, but occasionally you do hit its limits. So I think the biggest area of Keras that could be improved would be extensibility, using its backend interface. Another thing I am excited about is the contrib.keras package in TensorFlow, I think it might open up even more opportunity for customization, or at least the potential to maybe mix and match TensorFlow with Keras.

It comes as no surprise to many developers, Python has grown to become the preferred language of choice for data science. One of the reasons for its staggering adoption in the data science community is the rich suite of libraries for effective data analysis and visualization - allowing you to extract useful, actionable insights from your data. Pandas is one such Python-based library, that provides a solid platform to carry out high-performance data analysis. Ted Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data. Ted received his Master’s degree in statistics from Rice University and has used his analytical skills to play poker professionally. He taught math before becoming a data scientist. He is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow. In this exciting interview, Ted takes us through an insightful journey into pandas - Python’s premier library for exploratory data analysis, and tells us why it is the go-to library for many data scientists to discover new insights from their data. Key Takeaways Data scientists are in the business of making predictions. To make the right predictions you must know how to analyse your data. to perform data analysis efficiently, you must have a good understanding of the concepts as well be proficient using the tools like pandas. Pandas Cookbook contains step by step solutions to the master the pandas syntax while going through the data exploration journey (missteps et al) to solve the most common and not-so-common problems in data analysis. Unlike R which has several different packages for different data science tasks, pandas offers all data analysis capabilities as a single large Python library. Pandas has good time-series capabilities, making it well-suited for building financial applications. That said, its best use is in data exploration - to find interesting discoveries within the data. Ted says beginners in data science should focus on learning one data science concept at a time and master it thoroughly, rather than getting an overview of multiple concepts at once. Let us start with a very fundamental question - Why is data crucial to businesses these days? What problems does it solve? All businesses, from a child’s lemonade stand to the largest corporations, must account for all their operations in order to be successful. This accounting of supplies, transactions, people, etc., is what we call ‘data’ and gives us historical records of what has transpired in a business. Without this data, we would be reduced to oral history or what humans used for accounting before the advent of writing systems. By collecting and analyzing data, we gain a deeper understanding of how the business is progressing. In the most basic instances, such as with a child’s lemonade stand, we know how many glasses of lemonade have been sold, how much was spent on supplies, and importantly whether the business is profitable. This example is incredibly trivial, but it should be noted that such simple data collection is not something that comes naturally to humans. For instance, many people have a desire to lose weight at some point in their life, but fail to accurately record their daily weight or calorie intake in any regular manner, despite the large number of free services available to help with this. There are so many Python-based libraries out there which can be used for a variety of data science tasks. Where does pandas fit into this picture? pandas is the most popular library to perform the most fundamental tasks of a data analysis. Not many libraries can claim to provide the power and flexibility of pandas for working with tabular data. How does pandas help data scientists in overcoming different challenges in data analysis? What advantages does it offer over domain-specific languages such as R? One of the best reasons to use pandas is because it is so popular. There are a tremendous amount of resources available for it, and an excellent database of questions and answers on StackOverflow. Because the community is so large, you can almost always get an immediate answer to your problem. Comparing pandas to R is difficult as R is an entire language that provides tools for a wide variety of tasks. Pandas is a single large Python library. Nearly all the tasks capable in pandas can be replicated with the right library in R. We would love to hear your journey as a data scientist. Did having a master's degree in statistics help you in choosing this profession? Also tell us something about how you leveraged analytics in professional Poker! My journey to becoming a “data scientist” began long before the term even existed. As a math undergrad, I found out about the actuarial profession, which appealed to me because of its meritocratic pathway to success. Because I wasn’t certain that I wanted to become an actuary, I entered a Ph.D. program in statistics in 2004, the same year that an online poker boom began. After a couple of unmotivating and half-hearted attempts at learning probability theory, I left the program with a masters degree to play poker professionally. Playing poker has been by far the most influential and beneficial resource for understanding real-world risk. Data scientists are in the business of making predictions and there’s no better way to understand the outcomes of predictions you make than by exposing yourself to risk. Your recently published 'pandas Cookbook' has received a very positive response from the readers. What problems in data analysis do you think this book solves? I worked extremely hard to make pandas Cookbook the best available book on the fundamentals of data analysis. The material was formulated by teaching dozens of classes and hundreds of students with my company Dunder Data and my meetup group Houston Data Science. Before getting to what makes a good data analysis, it’s important to understand the difference between the tools available to you and the theoretical concepts. Pandas is a tool and is not much different than a big toolbox in your garage. It is possible to master the syntax of pandas without actually knowing how to complete a thorough data analysis. This is like knowing how to use all the individual tools in your toolbox without knowing how to build anything useful, such as a house. Similarly, understanding theoretical concepts such as ‘split-apply-combine’ or ‘tidy data’ without knowing how to implement them with a specific tool will not get you very far. Thus, in order to make a good data analysis, you need to understand both the tools and the concepts. This is what pandas Cookbook attempts to provide. The syntax of pandas is learned together with common theoretical concepts using real-world datasets. Your readers loved the way you have structured the book and the kind of datasets, examples and functions you have chosen to showcase pandas in all its glory. Was is experience, intuition, or observations that led to this fantastic writing insight? The official pandas documentation is very thorough (well over 1,000 pages) but does not present the features as you would see them in a real data analysis. Most of the operations are shown in isolation on contrived or randomly generated data. In a typical data analysis, it is common for many pandas operations to be called one after another. The recipes in pandas Cookbook expose this pattern to the reader, which will help them when they are completing an actual data analysis. This is not meant to disparage the documentation as I have read it multiple times myself and recommend reading it along with pandas Cookbook. Quantitative finance is one domain where pandas finds major application. How does pandas help in developing better financial applications? In what other domains does pandas find important applications and how? Pandas has good time-series capabilities which makes it well-suited for financial applications. It’s ability to group by specific time periods is a very useful feature. In my opinion, pandas most important application is with exploratory data analysis. It is possible for an analyst to quickly use pandas to find interesting discoveries within the data and visualize the results with either matplotlib or Seaborn. This tight integration, coupled with the Jupyter Notebook interface make for an excellent ecosystem for generating and reporting results to others. Please tell us more about 'pandas Cookbook'. What in your opinion are the 3 major takeaways from it? Are there any prerequisites needed to get the most out of the book? The only prerequisite for pandas Cookbook is a fundamental understanding of the Python programming language. The recipes progress in difficulty from chapter to chapter and for those with no pandas experience, I would recommend reading it cover to cover. One of the major takeaways from the book is to be able to write modern and idiomatic pandas code. Pandas is a huge library and there are always multiple ways of completing each task. This is more of a negative than a positive as beginners notoriously write poorly written and inefficient code. Another takeaway is the ability to probe and investigate data until you find something interesting. Many of the recipes are written as if the reader is experiencing the discovery process alongside the author. There are occasional (and purposeful) missteps in some recipes to show how often the right course of action is not always known. Lastly, I wanted to teach common theoretical concepts of doing a data analysis while simultaneously learning pandas syntax. Finally, what advice would you have for beginners in data science? What things should they keep in mind while designing and developing their data science workflow? Are there any specific resources which they could refer to, apart from this book of course? For those just beginning their data science journey, I would suggest keeping their ‘universe small’. This means concentrating on as few things as possible. It is easy to get caught up with a feeling that you need to keep learning as much as possible. Mastering a few subjects is much better than having a cursory knowledge of many. If you found this interview to be intriguing, make sure you check out Ted’s pandas Cookbook which presents more than 90 unique recipes for effective scientific computation and data analysis.    

Mckinsey report predicts that artificial intelligence techniques including deep learning and reinforcement learning have the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. Reinforcement learning (RL) is an increasingly popular technique for enterprises that deal with large complex problem spaces. It enables the agents to learn from their own actions and experiences. When working in an interactive environment, they use a trial and error process to find the best-optimized result. Reinforcement learning is at the cutting-edge right now and it's finally reached a point that it can be applied to real-world industrial systems. We recently interviewed Sudharsan Ravichandiran, a data scientist at param.ai, and the author of the book, Hands-On Reinforcement Learning with Python. Sudharsan takes us on an insightful journey explaining to us why reinforcement learning is trending and becoming so popular lately. He talks about the positive contributions of RL in various research fields such as gaming industry, robotics, inventory management, manufacturing, and finance. Author’s Bio Sudharsan Ravichandiran, author of the book, Hands-On Reinforcement Learning with Python is a data scientist, researcher, and YouTuber. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow. You can follow his open source contributions on GitHub. Key Takeaways Reinforcement learning adoption among the community has increased exponentially because of the augmentation of reinforcement learning with state of the art deep learning algorithms. It is extensively used in the Gaming industry, robotics, Inventory management, and Finance. You can see more and more research papers and applications leading to full-fledged self-learning agents. One of the common challenges faced in RL is safe exploration. To avoid this problem, one can use imitation learning (learning from human demonstration) to provide the best-optimized solution. Deep meta reinforcement learning will be the future of artificial intelligence where we will implement artificial general intelligence (AGI) to build a single model to master a wide variety of tasks. Thus each model will be capable to perform a wide range of complex tasks. Sudharsan suggests the readers should learn to code the algorithms from scratch instead of using libraries. It will help them understand and implement complex concepts in their research work or projects far better. Full Interview Reinforcement learning is at the cutting-edge right now, with many of the world’s best researchers working on improving the core algorithms. What do you think is the reason behind RL success and why RL is getting so popular lately? Reinforcement learning has been around for many years, the reason it is so popular right now is because it is possible to augment reinforcement learning with state of the art deep learning algorithms. With deep reinforcement learning, researchers have obtained better results. Specifically, reinforcement learning started to grow on a massive scale after the reinforcement learning agent, AlphaGo, won over the world champion in a board game called AlphaGo. Also, Deep reinforcement learning algorithms help us in taking a closer step towards artificial general intelligence which is the true AI. Reinforcement learning is a pretty complex topic to wrap your head around, what got you into RL field? What keeps you motivated to keep on working on these complex research problems? I used to be a freelance web developer during my university days. I had a paper called Artificial Intelligence on my Spring semester, it really got me intrigued and made me want to explore more about the field. Later on, I got invited to Microsoft data science conference where I met many experts and learned more about the field way better. All these got me intriguing and made me to venture into AI. The one thing which motivates and keeps me excited are the advancements happening in the field of reinforcement learning lately. DeepMind and OpenAI are doing a great job and massively contributing to the RL community. Recent advancements like human-like robot hand control to manipulate physical objects with unprecedented dexterity, imagination augmented agents which can imagine and makes decisions, world models where the agents have the ability to dream excite me and keeps me going. Can you please list down 3 popular problem areas where RL is majorly used? Also, what are the 3 most current challenges faced while implementing RL in real-life? As a developer/researcher how you are gearing up to solve them? RL is predominantly used in the Gaming industry, robotics, and Inventory management. There are several challenges in Reinforcement learning. For instance, safe exploration. Reinforcement learning is basically a trial and error process where agents try several actions to find the best and optimal action. Consider an agent learning to navigate/learning to drive a car. Agents don't know which action is better unless they try them. The agent also has to be careful in not selecting actions which are harmful to others or itself, say, for example, colliding with other vehicles. To avoid this problem, we can use imitation learning or learning from a human demonstration where the agents learn directly from the human supervisor. Apart from these, there are various evolutionary strategies used to solve the challenges faced in RL. There are few positive developments in RL happened from Open AI and DeepMind team that have got widely adopted both in research and in real-world applications. What are some cutting-edge techniques you foresee getting public attention in RL in 2018 and in the near future?   Great things are happening around RL research each and every day. Deep Meta reinforcement learning will be the future of AI where we will be so close to achieving artificial general intelligence (AGI). Instead of creating different models to perform different tasks, with AGI, a single model can master a wide variety of tasks and mimics the human intelligence. Gaming and robotics or simulations are the two popular domains where reinforcement learning is extensively used. In what other domains does RL find important use cases and how? Manufacturing In manufacturing, intelligent robots are used to place objects in the right position. If it fails or succeeds in placing the object in the right position, it remembers the action and trains itself to do this with greater accuracy. The use of intelligent agents will reduce labor costs and result in better performance. Inventory management RL is extensively used in inventory management, which is a crucial business activity. Some of these activities include supply chain management, demand forecasting, and handling several warehouse operations (such as placing products in warehouses for managing space efficiently). Infrastructure management RL is also used in infrastructure management. For an instance, Google researchers in DeepMind have developed RL algorithms for efficiently reducing the energy consumption in their own data center. Finance RL is widely used in financial portfolio management, which is the process of constant redistribution of a fund into different financial products and also in predicting and trading in commercial transactions markets. JP Morgan has successfully used RL to provide better trade execution results for large orders. Your recently published ‘Hands-On Reinforcement Learning with Python‘ has received a very positive response from the readers. What are some key challenges in learning reinforcement learning and how does your book help them? One of the key challenges in learning reinforcement learning is the lack of intuitive examples and poor understanding of RL fundamentals with required math. The book addresses all the challenges by explaining all the reinforcement learning concepts from scratch and gradually takes readers to advanced concepts by exploring them one at a time. The book also explains all the required math step by step intuitively along with plenty of examples. My intention behind adding multiple examples and code to each chapter was to help the readers understand the concepts better. This will also help them in understanding when to apply a particular algorithm. This book also works as a perfect reference for beginners who are new to reinforcement learning. Are there any prerequisites needed to get the most out of the book? What do you think they should keep in mind while developing their own self-learning agents? Readers who are familiar with machine learning and Python basics can easily follow the book. The book starts with explaining reinforcement learning fundamentals and reinforcement learning algorithms with applications and then it takes the reader in understanding deep learning algorithms followed by the book explaining advanced deep reinforcement learning algorithms. While creating self-learning agents, one should be careful in designing reward and goal functions. What in your opinion are the 3-5 major takeaways from your book? The book serves as a solid go-to place for someone who wants to venture into deep reinforcement learning. The book is completely beginner friendly and takes the readers to the advanced concepts gradually. At the end of the book, the readers can master reinforcement learning, deep learning and deep reinforcement learning along with their applications in TensorFlow and all the required math. Would you like to add anything more to our readers? I would suggest the readers code the algorithms from scratch instead of using libraries, it will help them in understanding the concepts far better. I also would like to thank each and every reader for making this book a huge success. My best wishes to them for their reinforcement learning projects. If you found this interview to be interesting, make sure you check out other insightful articles on reinforcement learning: Top 5 tools for reinforcement learning This self-driving car can drive in its imagination using deep reinforcement learning Dopamine: A Tensorflow-based framework for flexible and reproducible Reinforcement Learning research by Google OpenAI builds reinforcement learning based system giving robots human like dexterity DeepCube: A new deep reinforcement learning approach solves the Rubik’s cube with no human help

This week I published a post that explored some of the key trends in software infrastructure that security engineers, SREs, and SysAdmins should be paying attention to in 2019. There was clearly a lot to discuss - which is why I sat down with my colleague Stacy Matthews to discuss some of the topics explored in the post in a little more. Enjoy! https://soundcloud.com/packt-podcasts/why-observability-and-chaos-engineering-will-be-vital-in-2019 What do you think? Is chaos engineering too immature for widespread adoption? And how easy will it be to begin building for observability?

Last month, Fastly, a provider of an edge cloud platform, introduced a collection of resources to help developers learn the ins and outs of popular cloud solutions. The collection consists of step-by-step tutorials and ready-to-deploy code that developers can customize, and deploy to their Fastly configuration. We had the opportunity to interview Adam Denenberg, Fastly’s SVP of Customer Solutions, to get more insight into this particular project and other initiatives Fastly is taking to empower developers. We also grabbed this opportunity to talk to Denenberg about the emergence and growth of edge computing and fog computing and what it all means for the industry. What are the advantages of edge computing over cloud? Cloud computing is a centralized service that provides computing resources including servers, storage, databases, networking, software, analytics, and intelligence on demand. It is flexible, scalable, enables faster innovation, and has revolutionized the way people store and interact with data. However, because it is a centralized system, it can cause issues such as higher latency, limited bandwidth, security issues, and the requirement of high-speed internet connectivity. This is where edge computing comes in - to address these limitations. In essence, it’s a decentralized cloud. “Edge computing is the move to put compute power and logic as close to the end-user as possible. The edge cloud uses the emerging cloud computing serverless paradigm in which the cloud provider runs the server and dynamically manages the allocation of machine resources,” Denenberg explains. When it comes to making real-time decisions edge computing, can be very effective. He adds, “The average consumer expects speedy online experiences, so when milliseconds matter, the advantage of processing at the edge is that it is an ideal way to handle highly dynamic and time-sensitive data quickly. “In contrast, running modern applications from a central cloud poses challenges related to latency, ability to pre-scale, and cost-efficiency.” What is the difference between fog computing and edge computing? Fog computing and edge computing can appear very similar. They both involve pushing intelligence and processing capabilities closer to the origin of data. However, the difference lies in where the location of intelligence and compute power is placed. Explaining the difference between the two, Denenberg said, “fog computing, a term invented by Cisco, shares some similar design goals as edge computing, such as reducing latency to the end-user request and providing access to compute resources in a decentralized model. After that, things begin to differ.” He adds, “On the one hand, fog computing has a focus on use cases like IoT and sensors. This allows enterprises to extend their network from a central cloud closer to their devices and sensors, while maintaining a reliance on the central cloud. “Edge computing, on the other hand, is also about moving compute closer to the end-user, but doing so in a way that removes the dependency on the central cloud as much as possible. By collocating compute and storage (cache) on Fastly’s edge cloud, our customers are able to build very complex, global-scale applications and digital experiences without any dependency on a centralized compute resources.” Will edge computing replace cloud computing? A short answer to this question would be “not really.” “I don’t think anything at this moment will fully replace the central cloud,” Denenberg explains. “People said data centers were dead as soon as AWS took off, and, while we certainly saw a dramatic shift in where workloads were being run over the last decade, plenty of organizations still operate very large data centers. “There will continue to be certain workloads such as large-scale offline data processing, data warehouses, and the building of machine learning models that are much more suited to an environment that requires high compute density and long and complex processing times that operate on extremely massive data sets with no time sensitivity.” What is Fastly? Fastly’s story started back in 2008 when Artur Bergman, its founder, was working at Wikia. Three years later, he founded Fastly, headquartered in San Francisco, with its branches in four cities including London, Tokyo, New York, and Denver. Denenberg shared that Fastly’s edge cloud platform was built to address the limitations in content delivery networks (CDNs). “Fastly is an edge cloud platform built by developers, to empower developers. It came about as a result of our founder Artur Bergman's experience leading engineering at Wikia, where his passion for delivering fast, reliable, and secure online experiences for communities around the world was born. So he saw firsthand that CDNs -- which were supposed to address this problem -- weren't equipped to enable the global, real-time experiences needed in the modern era.” He further said, “To ensure a fast, reliable, and secure online experience, Fastly developed an edge cloud platform designed to provide unprecedented, real-time control, and visibility that removes traditional barriers to innovation. Knowing that developers are at the heart of building the online experience, Fastly was built to empower other developers to write and deploy code at the edge. We did this by making the platform extremely accessible, self-service, and API-first.” Fastly’s new edge cloud resources Coming to Fastly’s new edge cloud resources, Denenberg shared the motivation behind this launch. He said, “We’re here to serve the developer community and allow them to dream bigger at the edge, where we believe the future of the web will be built. This new collection of recipes and tutorials was born out of countless collaborations and problem-solving discussions with Fastly's global community of customers. Fastly's new collection of edge cloud resources make it faster and safer for developers to discover, test, customize, and deploy edge cloud solutions.” Currently, Fastly has shared 66 code-based edge cloud solutions covering aspects like authentication, image optimization, logging, and more. It plans to add more solutions to the list in the near future. Denenberg shared, “Our initial launch of 66 recipes and four solution patterns were created from some of the most common and valuable solutions we’ve seen when working with our global customer base. However, this is just the beginning - many more solutions are on our radar to launch on a regular cadence. This is what has us really excited-- as we expose more of these solutions to customers, the more inspiration they have to go even further in their work, which creates a remarkable flywheel of innovation on our edge cloud.” Challenges when developing on the edge When asked about what edge cloud solutions Denenberg thinks developers often find difficult, he said, “I think difficulty is a tricky thing to address because engineering is a lot of times about tradeoffs. Those tradeoffs are most often realized when pursuing instant scalability, being able to run edge functions everywhere, and achieving low latency and microsecond boot time. He adds, “NoSQL saw tremendous growth because it presented the ability to achieve scale with very reasonable trade-offs based on the types of applications people were building that traditional SQL databases made very difficult, from an architectural perspective, like scaling writes linearly to a cluster easily, for example. So for me, given the wide variety of applications our customers can build, I think it’s about taking advantage of our platform in a way that improves the overall user experience, which sometimes just requires a shifting of the mindset in how those applications are architected.” We asked Denenberg whether other developers will be able to pitch in to expand this collection of resources. “We are already talking with customers who are excited to share what they have built on our platform that might allow others to achieve enhanced online experiences for their end users,” he told us. “Fastly has an internal team dedicated to reviewing the solutions customers are interested in sharing to ensure they have the same consistency and coding style that mirrors how we would publish them internally. We welcome the sharing of innovation from our customer base that continues to inspire us through their work on the edge.” Other initiatives by Fastly to empower developers Fastly is continuously contributing towards making the internet more trustworthy and safer by getting involved in projects like QUIC, Encrypted SNI, and WebAssembly. Last year, Fastly made three of its projects available on Fastly Labs: Terrarium, Fiddle, and Insights. Read also: Mozilla introduces Neqo, Rust implementation for QUIC, new http protocol Denenberg shared that there are many ways Fastly is contributing to the open source community. “Yes, empowering developers is at the forefront of what we do. As developers are familiar with the open-source caching software that we use, it makes adopting our platform easier. We give away free Fastly services to open source and nonprofit projects. We also continue to work on open source projects, which empower developers to build applications in multiple languages and run them faster and more securely at our edge.” Fastly also constantly tries to improve its edge cloud platform to meet its customers’ needs and empower them to innovate. “As an ongoing priority, we work to ensure that developers have the control and insight into our edge platform they need. To this end, our programmable edge provides developers with real-time visibility and control, where they can write and deploy code to push application logic to the edge. This supports modern application delivery processes and, just as importantly, frees developers to innovate without constraints,” Denenberg adds. He concludes, “Finally, we believe our values empower our community in several ways. At Fastly, we have chosen to grow with a focus on transparency, integrity, and inclusion. To do this, we are building a kind, ethical, and inclusive team that reflects our diverse customer base and the diversity of the developers that are creating online experiences. The more diverse our workforce, the easier it is to attract diverse talent and build technology that provides true value for our developer community across the world.” Follow Adam Denenberg on Twitter: @denen Learn more about Fastly and its edge-cloud platform at Fastly’s official website. More on cloud computing Cloud Next 2019 Tokyo: Google announces new security capabilities for enterprise users Google Cloud and Nvidia Tesla set new AI training records with MLPerf benchmark results How do AWS developers manage Web apps?

Jason De Oliveira works as a CTO for MEGA International, a software company in Paris (France), providing modeling tools for business transformation, enterprise architecture, and enterprise governance, risk, and compliance management. He is an experienced manager and senior solutions architect, with high skills in software architecture and enterprise architecture. He has been awarded him MVP C#/.NET by Microsoft for over 6 years for his numerous contributions to the Microsoft community. In this interview, Jason talks about the newly introduced features of .NET Core 2.0 and how they empower effective cross-platform application development. He also gives us a sneak-peek of his recently released book Learning ASP.NET Core 2.0 Packt: Let's start with a very basic question. What is .NET Core? How is it different from the .NET Framework? Jason De Oliveira: In the last 20 years, Microsoft has focused mainly on Windows and built many technologies around it. You can see that easily when looking at the ASP.NET and the .NET Framework in general. They provide a very good integration and extend the operating system for building great desktop and web applications, but only on the Windows platform. In the last 5 years, we have seen some major changes in the overall strategy and vision of Microsoft due to major changes in the IT market. The cloud-first as well as the mobile-first strategy coupled with the support for other operating systems has lead Microsoft to completely rethink most of the existing frameworks - including the .NET Framework. That is one of the major reasons why the .NET Core framework came into existence. It incorporates a new way of building applications, fully embracing multi-platform development and the latest standards and technologies - and what a great framework it has turned out to be! ASP .Net Core 2.0 was recently announced in August 2017. What are the new features and updates introduced in this release to make the development of web apps easier? The latest version of ASP.NET Core is 2.0, which, provides much better performance than the other versions before it. It has been open-sourced, so developers can understand how it works internally and adapt it easily to specific needs if necessary. Furthermore, the integration between .NET Core and Visual Studio has been improved. Some other important features of this new release are: The Meta-Package which includes everything necessary to develop great web applications has been added to the framework An improved NuGet compatibility has been added. This leads to much better developer productivity and efficiency. Web development can be fun and using .NET Core in conjunction with the latest version of Visual Studio proves it! What benefits does Entity Framework Core 2 offer while building a robust MVC web app? When you are building web applications, you need to store your data somewhere. Today, most of the time the data is stored in relational databases (Microsoft SQL Server, Oracle and so on). While there are multiple ways of connecting to a database using ASP.NET Core, we advise using Entity Framework Core 2, because of its simplicity and because it contains all necessary features already built-in. Another big advantage is that it abstracts the database structure from the object-oriented structure within your code (also commonly called ORM). It furthermore supports nearly all databases you can find on the market either directly or via additional providers. When it comes to .NET Core, do you think there is any scope for improvement? What can Microsoft do in the future to improve the suite? To be honest ASP.NET Core 2.0 has achieved a very high level of feature coverage. Nearly everything you can think of is included by default, which is quite remarkable. Nevertheless, Microsoft has already shipped an updated version called ASP.NET Core 2.1 and we can expect that it will further support and evolve the framework. However, an area of improvement could be Artificial Intelligence (AI). As you might know, Microsoft is currently investing very strongly in this area and we think that we might see some features getting included with the next versions of ASP.NET Core. Also in terms of testability of code and especially live unit testing, we hope to see some improvements in the future. Unit tests are important for building high quality application with less bugs, so having a thorough integration of ASP.NET Core with the Visual Studio Testing tools would be a big advantage for any developer. Tell us something about your book. What makes it unique? With this book, you will learn and have fun at the same time. The book is very easy to read, while containing basic and advanced concepts of ASP.NET Core 2.0 web development. It is not only about the development, though. You will also see how to apply agile methodologies and use tools such as Visual Studio and Visual Studio Code. The examples in the book are real world examples, which have been added to build a real application at the end. Not just stripped down sample examples, but instead examples that you can adapt to your own application needs quickly. From the start to the end of the book we have applied a Minimum Viable Product (MVP) approach, meaning that at the end of each chapter you will have evolved the overall sample application a little bit more. This is motivating and interesting and will keep you hooked. You will see how to work in different environments such as on-premises and in the cloud. We explain how to deploy, manage and supervise your ASP.NET Core 2.0 web application in Microsoft Azure, Amazon and Docker, for example. For someone new to web development, what learning path in terms of technologies would you recommend? What is the choice of tools he/she should make to build the best possible web applications? Where does .NET Core 2.0 fit into the picture? There are different types of web developers, who could take potentially different paths. Some will start with the graphical user interface and be more interested in the graphical representation of a web application. They should start with HTML5 and CSS3 and maybe use JavaScript to handle the communication with the server. Then, there are API developers, who are not very interested in graphical representation but more in building efficient API and web services, which allow reducing bandwidth and providing good performance. In our book, we show the readers how to build views and controllers, while using the latest frontend technologies to provide a modern look and feel. We then explain how to use the built-in features of ASP.NET Core 2.0 for building Web APIs and how to connect them via Entity Framework 2 with the database or provide a whole host of other services (such as sending emails, for example).