Tech News

Stack Overflow’s 2018 survey has just been released. It contains some great insights into the way developers live and work. It’s also confirmed plenty of things that have been commonplaces in the tech industry. So, here are the 10 most predictable findings from this year’s survey... JavaScript is the most popular programming language We probably shouldn’t be surprised - JavaScript has come out as the most popular language in the Stack Overflow survey since 2013. And with JavaScript now cemented as the foundational language of the web, it’s unlikely we’re going to see it knocked off its perch any time soon. With the growth of tools like NodeJS extending JavaScript’s capabilities, it’s only going to consolidate its position further. Developers are on Stack Overflow all the time This is a pretty comforting stat for the team at Stack Overflow - they’re the go-to home of almost everyone that works with code on the planet. 32.5% of respondents claimed they check Stack Overflow ‘daily or almost daily’, 31% check it multiple times a day. A further 22% check the site at least once a week. Developers are self-taught and write code outside of work This is something we’re well aware of at Packt - developers are autodidacts. That’s probably partly the nature of working with software that can be so changeable and the nature of developers themselves - curious and exploratory. 86.7% of survey respondents taught themselves a new language, tool or framework outside of any formal setting. The fact that formal education is so rarely sought or provided for explains the fact that so many developers write code outside of work. Stack Overflow’s 2018 survey reports than more than 80% of developers code as a hobby. Git is easily the most popular version control tool This was a sure thing - and the survey stats prove it. 87.2% of respondents reported Git as their version control tool of choice. Despite murmurs online that it’s unnecessarily complicated (the search suggestion next to ‘why is Git so popular’ is ‘why is Git so complicated’), thanks to its powerful scalable features and, of course, the dominance of GitHub in the lives of technical people, it’s become a core part of developer life. Python is the fastest growing programming language on the planet We know just how popular Python is at Packt. We joke that we should just be the Python Publishing Company. This year it has risen above C#, after last year rising above PHP. Where will its rise end? Or, as more and more people need to write code, will it become the de facto language of technology-powered professionals? TensorFlow is getting lots of love TensorFlow was 2018’s most loved tool with more than 73% of developers using it expressing a great deal of enthusiasm for it. Clearly its delivering on its tagline: ‘machine learning for everyone’. React is the most in-demand tool If TensorFlow is the most loved tool, we also weren’t that surprised to see that React is massively in demand. 21.3% of developers who don’t currently use it expressed an interest in using it. Node.js was in second place with 20.9%. The love for TensorFlow must be getting out there as that came in third here, with 15.5% of respondents interested in developing with it. People hate using Visual Basic It’s not hard to find someone complaining about Visual Basic online. The ecosystem is widening, and there are far more attractive options for developers out there. Microsoft are even self-consciously changing their strategy with VB, as this post from last year outlines. Stack Overflow reports that this is the third year in a row that Visual Basic has come out as the ‘most dreaded’ programming language, meaning people using it ‘express no interest in doing so’. Job churn is a fact of life in tech 34.6% of survey respondents changed jobs less than a year ago. A further 22% changed jobs between 1 and 2 years ago. Like technology itself, change is a constant in the lives of many developers - for better and worse. Agile and Scrum are the most popular software development methodologies No surprises here - these methodologies have become embedded in the everyday practices of millions of developers.

Scala 2.12.5 version has been released. Scala is a popular programming language for a data scientist. It is mostly favored by aspiring or seasoned data scientists who are planning to work with Apache Spark for Big Data analysis. With the new version 2.12.5, Scala has brought in four major highlights. Most importantly, Scala 2.12.5 is binary compatible with the whole Scala 2.12 series. Major highlights: When compiling on Java 9 or higher, the new-release N flag changes the compilation classpath to match the JDK version N. This works for the JDK itself and for multi-release JARs on the classpath. With the new -Ybackend-parallelism N compiler flag, the backend can now run bytecode serialization, classfile writing, and method-local optimizations (-opt:l:method) in parallel on N threads. The raw"" and s"" string interpolators are now intercepted by the compiler to produce more efficient bytecode. The -Ycache-plugin-class-loader and -Ycache-macro-class-loader flags enable caching of classloaders for compiler plugins and macro definitions. This can lead to significant performance improvements. Other features include: The apply method on the PartialFunction companion object is now deprecated. Scala JARs (library, reflect, compiler) now have an Automatic-Module-Name attribute in their manifests. Enabling unused warnings now lead to fewer false positives. Explicit eta-expansion (foo _) of a nullary method no longer gives a deprecation warning. Scala releases are available through a variety of channels, including: Bump the scalaVersion setting in your sbt-based project Download a distribution from scala-lang.org Obtain JARs via Maven Central However, there is regression since 2.12.4 when compiling code on Java 9 or 10 that uses macros. Users must either compile on Java 8 or wait for 2.12.6. You can check out all closed bugs and merged PRs for further details.

D3.js version 5.0 released. D3.js is a JavaScript library for creating dynamic, interactive data visualizations in web browsers. The new version 5.0 includes only a few non-backwards-compatible changes. D3.js now uses Promises, instead of asynchronous callbacks to load data. Promises simplify the structure of asynchronous code, especially in modern browsers that support async and await. Let us see some of the changes in this version: The d3-request module has been replaced by d3-fetch, due to the adoption of Promises. D3 5.0 also deprecates and removes the d3-queue module. One can use Promise.all to run a batch of asynchronous tasks in parallel, or a helper library such as p-queue to control concurrency. D3 now includes d3-scale-chromatic, which implements excellent schemes from ColorBrewer, including categorical, diverging, sequential single-hue and sequential multi-hue schemes. The version 5.0 also provides implementations of marching squares and density estimation via d3-contour. There are two new d3-selection methods: selection.clone for inserting clones of the selected nodes, and d3.create for creating detached elements. D3’s package.json no longer pins exact versions of the dependent D3 modules. This fixes an issue with duplicate installs of D3 modules. To read more about the changes in detail, visit the GitHub repo.

Microsoft announced some significant spring updates in its products. These include the Common Data Services update to Power BI and Power Apps.    Common Data Service for Analytics comes to PowerBI The CDS for Analytics capability will reduce the complexity of driving business analytics across data from business apps and other sources. Its features include: Common data schema - Common Data Services for Analytics expands Power BI by introducing an extensible business application schema on which organizations can integrate data from multiple sources. Accelerated access to insights - With the new CDS for Analytics capability, customers will have the opportunity to purchase apps from Microsoft and its partners built on Power BI. App customization and extension - Users will be able to custom tailor reports or build new ones uniquely relevant to their needs using data services like Azure Machine Learning and Azure Databricks to self-service customizations in low code/no code experiences in Power BI, irrespective of their skillset. Bringing the power of Dynamics 365 to PowerApps Microsoft plans to amalgamate PowerApps with Business Application Platform, the platform that powers Dynamic 365. Microsoft took the Common Data Service, merged its features with the Dynamics 365 platform and renamed it to the Common Data Service for Apps to reflect the new functionality. The Common Data Service (CDS) would be adding capabilities such as server-side logic, business processes, advanced security and pro developer support to PowerApps. This update would also introduce a new style of app building known as model-driven apps. These apps would automatically generate rich user experience based on the data and processes in the Common Data Service. PowerApps built on the canvas get new capabilities for working with the Common Data Service as well. For these and the other updates rolled out by Microsoft, read the Microsoft Blog

This ICLR 2018 accepted paper, Multi-Scale Dense Networks for Resource Efficient Image Classification, introduces a new model to perform image classification with limited computational resources at test time. This paper is authored by Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Weinberger. The 6th annual ICLR conference is scheduled to happen between April 30 - May 03, 2018. Using a multi-scale convolutional neural network for resource efficient image classification What problem is the paper attempting to solve? Recent years have witnessed a surge in demand for applications of visual object recognition, for instance, in self-driving cars and content-based image search. This demand is because of the astonishing progress of convolutional networks (CNNs) where state-of-the-art models may have even surpassed human-level performance. However, most are complex models which have high computational demands at inference time. In real-world applications, computation is never free; it directly translates into power consumption, which should be minimized for environmental and economic reasons. Ideally, all systems should automatically use small networks when test images are easy or computational resources are limited and use big networks when test images are hard or computation is abundant. In order to develop resource-efficient image recognition, the authors aim to develop CNNs that slice the computation and process these slices one-by-one, stopping the evaluation once the CPU time is depleted or the classification sufficiently certain. Unfortunately, CNNs learn the data representation and the classifier jointly, which leads to two problems The features in the last layer are extracted directly to be used by the classifier, whereas earlier features are not. The features in different layers of the network may have a different scale. Typically, the first layers of deep nets operate on a fine scale (to extract low-level features), whereas later layers transition to coarse scales that allow global context to enter the classifier. The authors propose a novel network architecture that addresses both problems through careful design changes, allowing for resource-efficient image classification. Paper summary The model is based on a multi-scale convolutional neural network similar to the neural fabric, but with dense connections and with a classifier at each layer.  This novel network architecture, called Multi-Scale DenseNet (MSDNet), address both of the problems described above (of classifiers altering the internal representation and the lack of coarse-scale features in early layers) for resource-efficient image classification. The network uses a cascade of intermediate classifiers throughout the network. The first problem is addressed through the introduction of dense connectivity. By connecting all layers to all classifiers, features are no longer dominated by the most imminent early exit and the trade-off between early or later classification can be performed elegantly as part of the loss function. The second problem is addressed by adopting a multi-scale network structure. At each layer, features of all scales (fine-to-coarse) are produced, which facilitates good classification early on but also extracts low-level features that only become useful after several more layers of processing. Key Takeaways MSDNet, is a novel convolutional network architecture optimized to incorporate CPU budgets at test-time. The design is based on two high-level design principles, to generate and maintain coarse level features throughout the network and to interconnect the layers with dense connectivity. The final network design is a two-dimensional array of horizontal and vertical layers, which decouples depth and feature coarseness. Whereas in traditional convolutional networks features only become coarser with increasing depth, the MSDNet generates features of all resolutions from the first layer on and maintains them throughout. Through experiments, the authors show that their network outperforms all competitive baselines on an impressive range of budgets ranging from highly limited CPU constraints to almost unconstrained settings. Reviewer feedback summary Overall Score: 25/30 Average Score: 8.33 The reviewers found the approach to be natural and effective with good results. They found the presentation to be clear and easy to follow. The structure of the network was clearly justified. The reviewers found the use of dense connectivity to avoid the loss of performance of using early-exit classifier interesting. They appreciated the results and found them to be quite promising, with 5x speed-ups and same or better accuracy than previous models.  However, some reviewers pointed out that the results about the more efficient densenet* could be shown in the main paper.

D3.js v5.0 released, Microsoft spring updates for PowerBI and PowerApps, IBM launches cheaper Blockchain Starter Plan for smaller businesses, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day D3.js v5.0 released! Microsoft spring updates for PowerBI and PowerApps Other Data Science News at a Glance 1. Google announced that it is working on two separate Blockchain projects, a “tamper-proof” auditing system and a cloud operations platform. Read more on Coin Telegraph 2. IBM launches cheaper Blockchain Starter Plan for smaller businesses. The plan is still in Beta version and it will feature a free 30-day trial for businesses willing to use it. Read more on Cryptona 3. Microsoft Azure on how to unlock your data’s potential with Azure SQL Data Warehouse and Azure Databricks. Read more on Microsoft Azure blog 4. TiDB 2.0 RC3 is released. This release has great improvement in MySQL compatibility, SQL optimization and stability. Read more on PingCAP 5. Duco announced the launch of the Duco Cube Data Platform. This platform enables users to access up to ten years’ worth of results and exception data via a Hadoop-based big data infrastructure. Read more on Digital Journal  

MachineLabs has released the entire code base of their machine learning platform as open source under the MIT license.   Following this announcement, all work at the organization will happen completely publicly, giving everyone the chance to join the effort and fork and fiddle with the code. MachineLabs is an open source online platform for Machine Learning. It is accessible, sharable and explorable via the web. It has ready to use environments for main ML frameworks including Tensorflow, Theano, PyTorch, and Caffe. Also, it has an online code Editor and access to blazingly fast GPU hardware. Moreover, it can expose generated assets via API, enabling users to request trained models for browser-based front-end apps. Their core mission, MachineLabs says, “has always been to empower the Machine Learning community, we believe it is critical for us to be as open and transparent as possible.” The open source announcement is MachineLabs’ first move to make their ML platform 100% open and transparent and entirely owned and governed by the community. In addition, the community that creates, runs and funds the ML platform will essentially also own and steer the revenue generating MachineLabs service. In order for this to happen, they are looking for platforms such as Aragon and Habour to implement a decentralized governance model. MachineLabs researchers have also started working on a Machine Learning Online Course with a radical stance on simplicity to further help people getting started within the Machine Learning field. They have created a Developer Guide to make it as easy as possible for everyone to contribute to the platform. They also ask contributors to go through the Contributing Guidelines, to make every contribution as smooth as possible. To know more, about the open source announcement, go through the official MachineLabs blog.

DeepMind’s neural deletion, MachineLabs goes opensource, IBM’s Snap Machine Learning, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day MachineLabs, the browser-based machine learning platform, goes open source. Snap machine learning: The 46x faster than TensorFlow ML library by IBM. Other Data Science News at a Glance In a recent paper, DeepMind has used neuron deletion to analyze neural networks. They found that interpretable neurons are no more important than confusing neurons for image classification and networks that generalize better are harder to break. Read more on the DeepMind Blog. BokehPlots partners with NumFOCUS to launch another strong open-source visualization ecosystem. With the addition of Bokeh, the NumFOCUS fiscal sponsorship program now encompasses 22 open source scientific computing projects. Read more on the NumFOCUS Blog. Paperspace launches an AI Platform as a Service offering called Gradient, based on the serverless delivery model. Gradient removes the friction involved in launching and configuring GPU-backed VMs to train machine learning and deep learning models. Read more on Forbes. There are new features in InfluxDB Open Source Backup and Restore. Now, the open source backup utility provides the option to run both backup and restore functions on a live database. Read more on the Influx Data. BigchainDB has announced the name of their next release as BigchainDB 2.0. Bigchain 2.0 uses version 2.0 transactions, as documented in the IPDB Transaction Spec version 2.0. The release of BigchainDB 2.0 means that BigchainDB 1.x has reached the end of the line.   Read more on the BigchainDB Blog.

IBM claims that its new Snap Machine learning library is 46x faster than TensorFlow. IBM’s Snap Machine Learning (Snap ML) is an efficient, scalable machine-learning library that enables very fast training of generalized linear models. IBM demonstrated that this new library can eliminate the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. Much recently, the Snap ML library set a new benchmark by outperforming training time for ML models 46 times faster than TensorFlow. Source : IBM Research By using the online advertising dataset released by Criteo Labs, which includes more than 4 billion training examples, IBM trained a logistic regression classifier in 91.5 seconds. Prior to this, the best result for training the same model was bagged by TensorFlow, which trained the same model in 70 minutes on Google Cloud Platform. Snap ML library, allows more agile development faster and more fine-grained exploration of the hyper-parameter space enables scaling to massive datasets makes frequent retraining of models possible in order to adapt to events as they occur Source: Snap Machine Learning Research paper Let’s take a look at the three distinct features of Snap ML library: Distributed training: This feature helps in building a data-parallel framework. This allows one to scale out and train on massive datasets that exceed the memory capacity of a single machine, which is crucial for large-scale applications. GPU acceleration: Implementation of specialized solvers designed to leverage the massively parallel architecture of GPUs while respecting the data locality in GPU memory in order to avoid large data transfer overheads. Sparse data structures: Many machine learning datasets are sparse, therefore some new optimizations have been enrolled for the algorithms used in IBM’s own system when applied to sparse data structures. Read more about this exciting news in detail on IBM Research.

On March 20, 2018, IBM launched its brand new Deep Learning as a Service (DLaaS) program for AI developers. The Deep Learning as a Service, which runs on IBM Watson, is an experiment-centric model training environment. This means users don’t have to worry about getting bogged down with planning and managing training runs themselves. Instead, the entire training life-cycle is managed automatically and the results can be viewed in real-time and can also be revisited later. The DLaaS service allows data scientists to train models using the resources they need and they simply have to pay only for the GPU time. Users can train their neural networks using a range of deep learning frameworks such as TensorFlow, PyTorch, and Caffe, without the need to buy and maintain the hardware cost. In order to use the service, users just have to prepare their data, upload it, begin training, then download the training results. This can potentially snip days or weeks off of training times. For instance, if a single GPU setup takes nearly a week to train visual image processing neural network on a couple million pictures, the time taken to do the same thing can be cut down to mere hours with this new cloud solution. Also, maintaining deep learning systems requires manpower. By investing time in IBM’s DLaaS can scale projects, which can result into clustering just a few GPUs for deep learning models. This experience is an entirely different skill set than training neural networks. To read more about this in detail, check out IBM’s blog post.  

Meta Learning, an original concept of cognitive psychology, is now applied to machine learning techniques. If we go by the social psychology definition, meta learning is the state of being aware of and taking control of one's own learning. Similar concepts, when applied to the machine learning theory states that a meta learning algorithm uses prior experience to change certain aspects of an algorithm, such that the modified algorithm is better than the original algorithm. To explain in simple terms, meta-learning is how the algorithm learns how to learn. Meta Learning: Making a versatile AI agent Current AI Systems excel at mastering a single skill, playing Go, holding human-like conversations, predicting a disaster, etc. However, now that AI and machine learning is possibly being integrated in everyday tasks, we need a single AI system to solve a variety of problems. Currently, a Go Player, will not be able to navigate the roads or find new places. Or an AI navigation controller won’t be able to hold a perfect human-like conversation. What machine learning algorithms need to do is develop versatility – the capability of doing many different things. Versatility is achieved by intelligent amalgamation of Meta Learning along with related techniques such as reinforcement learning (finding suitable actions to maximize a reward), transfer learning (re-purposing a trained model for a specific task on a second related task), and active learning (learning algorithm chooses the data it wants to learn from). Such different learning techniques provides an AI agent with the brains to do multiple tasks without the need to learn every new task from scratch. Thereby making it capable of adapting intelligently to a wide variety of new, unseen situations. Apart from creating versatile agents, recent researches also focus on using meta learning for hyperparameter and neural network optimization, fast reinforcement learning, finding good network architectures and for specific cases such as few-shot image recognition. Using Meta Learning, AI agents learn how to learn new tasks by reusing prior experience, rather than examining each new task in isolation. Various approaches to Meta Learning algorithms A wide variety of approaches come under the umbrella of Meta-Learning. Let's have a quick glance at these algorithms and techniques: Algorithm Learning (selection) Algorithm selection or learning, selects learning algorithms on the basis of characteristics of the instance. For example, you have a set of ML algos (Random Forest, SVM, DNN), data sets as the instances and the error rate as the cost metric. Now, the goal of Algorithm Selection is to predict which machine learning algorithm will have a small error on each data set. Hyper-parameter Optimization Many machine learning algorithms have numerous hyper-parameters that can be optimized. The choice of selecting these hyper-parameters for learning algorithms determines how well the algorithm learns.  A recent paper, "Evolving Deep Neural Networks", provides a meta learning algorithm for optimizing deep learning architectures through evolution. Ensemble Methods Ensemble methods usually combine several models or approaches to achieve better predictive performance. There are 3 basic types – Bagging, Boosting, and Stacked Generalization. In Bagging, each model runs independently and then aggregates the outputs at the end without preference to any model. Boosting refers to a group of algorithms that utilize weighted averages to make weak learners into stronger learners. Boosting is all about “teamwork”. Stacked generalization, has a layered architecture. Each set of base-classifiers is trained on a dataset. Successive layers receive as input the predictions of the immediately preceding layer and the output is passed on to the next layer. A single classifier at the topmost level produces the final prediction. Dynamic bias selection In Dynamic Bias selection, we adjust the bias of the learning algorithm dynamically to suit the new problem instance. The performance of a base learner can trigger the need to explore additional hypothesis spaces, normally through small variations of the current hypothesis space. The bias selection can either be a form of data variation or a time-dependent feature. Inductive Transfer Inductive transfer describes learning using previous knowledge from related tasks. This is done by transferring meta-knowledge across domains or tasks; a process known as inductive transfer. The goal here is to incorporate the meta-knowledge into the new learning task rather than matching meta-features with a meta-knowledge base. Adding Enhancements to Meta Learning algorithms Supervised meta-learning:  When the meta-learner is trained with supervised learning. In supervised learning we have both input and output variables and the algorithm learns the mapping function from the input to the output. RL meta-learning: This algorithm talks about using standard deep RL techniques to train a recurrent neural network in such a way that the recurrent network can then implement its own Reinforcement learning procedure. Model-agnostic meta-learning: MAML trains over a wide range of tasks, for a representation that can be quickly adapted to a new task, via a few gradient steps. The meta-learner seeks an initialization that is not only useful for adapting to various problems, but also can be adapted quickly. The ultimate goal of any meta learning algorithm and its variations is to be fully self-referential. This means it can automatically inspect and improve every part of its own code. A regenerative meta learning algorithm, on the lines of how a lizard regenerates its limbs, would not only blur the distinction between the different variations as described above but will also lead to better future performance and versatility of machine learning algorithms.

Microsoft SQL Server Management Studio 17.6, IBM’s Deep Learning as a Service program, Intel’s nGraph, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day Azure Database Services are now generally available for MySQL and PostgreSQL. Microsoft announces the release of SSMS, SQL Server Management Studio 17.6. IBM rolls out Deep Learning as a Service (DLaaS) program for AI developers. Other Data Science News at a Glance Intel AI open sources nGraph, a framework-neutral Deep Neural Network (DNN) model compiler. With nGraph, data scientists can focus on data science rather than worrying about how to adapt their DNN models to train and run efficiently on different devices. Read more on the Intel AI Blog. Databricks introduces a new millisecond low-latency mode of streaming called continuous mode in Apache Spark 2.3, now available in Databricks Runtime 4.0 as part of Databricks Unified Analytics Platform. Read more on the Databricks Blog. IBM announces the launch of IBM Watson Data Kits, which are designed to accelerate the development of AI applications. Watson Data Kits will provide companies with pre-enriched, machine-readable, industry-specific data for building AI projects. Read more on PR Newswire. DeepL introduces DeepL API, as part of DeepL Pro. DeepL API is an interface that allows other computer programs to send texts to DeepL servers and receive high-quality translations. Read more on the DeepL Blog. The Altair 2.0 release candidate is now available, with full support for interactive Vega-Lite visualizations in Python. Features a declarative grammar of interactions for building sophisticated interactive data views from easy-to-understand building blocks. Read more on Altair Github.

Microsoft recently announced, Azure Database services are generally available for database management systems, MySQL and PostgreSQL. This general availability would allow opportunities such as, Bringing the community versions of MySQL and PostgreSQL built-in high availability A 99.99% availability SLA Elastic scaling for performance Bringing industry leading security and compliance to Azure Some key features of the Azure Database for MySQL and PostgreSQL Provides a fully managed and an enterprise-ready community MySQL database-as-a-service for development and deployment of applications. One can easily lift and shift their workloads to the cloud, and use languages and frameworks of choice. A built-in high availability and capability to scale in seconds, which enables easy adjustment to changes in customer demands One can also benefit from the unparalleled security and compliance, including Azure IP advantage, as well as Azure’s industry leading reach with more data centers A flexible pricing model with choices in resources for ones workload Read more about this news on Microsoft official blog post

Microsoft has released SSMS 17.6, the latest generation of SQL Server Management Studio with support for SQL Server 2017. SQL Server Management Studio (SSMS) can be used to query, design, and manage databases and data warehouses, from the local computer, or in the cloud. It provides tools to configure, monitor, and administer instances of SQL. SSMS can be used to deploy, monitor, and upgrade the data-tier components of applications, as well as build queries and scripts. The SQL Server Management Studio, 17.6 has added a support for Azure SQL Database Managed Instance. This new flavor of Azure SQL Database provides, Near 100% compatibility with SQL Server on-premises. A native virtual network (VNet) implementation that addresses common security concerns. A business model favorable for on-premises SQL Server customers. Support for common management scenarios like creating, altering, backing, and restoring databases, importing, exporting, extracting and publishing Data-tier Applications, viewing and altering Server properties, support for full Object Explorer, SQL Agent jobs, Linked Servers and more. The SSMS 17.6 also features changes in the Object Explorer. Microsoft has added settings to not force brackets around names when dragging & dropping from Object Explorer to Query Window. They have also improved Integration Services by adding support to deploy packages to a SQL Database Managed Instance. In addition, SSMS 17.6 is labeled as Microsoft SQL Server Management Studio 17, and has a new icon. Source: Microsoft SSMS docs The version 17.6 of SSMS works with all supported versions of SQL Server 2008 - SQL Server 2017 and provides support for working with the cloud features in Azure SQL Database and Azure SQL Data Warehouse. Additionally, SSMS 17.6 can be installed side by side with SSMS 16.x or SQL Server 2014 SSMS and earlier. You can read the entire release notes on Microsoft SSMS docs.

Apple and IBM, the not so usual partners, contributed each of their renowned tech, Watson and CoreML in the collaboration to make the business applications running on Apple devices more intelligent. One can build one’s machine learning models using IBM Watson while using the data in an enterprise repository to train the model. After creating the model, one can run it through the CoreML converter tools and insert it in their Apple app. This allows the two partners to make the apps created under the partnership even smarter with machine learning. Apple developers seek a quick and easy way to build these apps and leverage the cloud where it’s delivered. IBM also announced a cloud console to simplify the connection between the Watson model building process and inserting that model in the application running on the Apple device. The app can share data back with Watson and improve the machine learning algorithm running on the edge device in a classic device-cloud partnership. The app runs in real-time and one does not need to be connected to Watson. However, as different parts on the device are classified, the data gets collected. Further, when one connects to Watson on a lower bandwidth, they can feed the data back in order to train their machine learning model and make it even better. Let’s take an instance where the app assists field technicians’ iPhones or iPads to scan the electrical equipment they are inspecting to automatically detect any anomalies. This would eliminate the need to send that data to IBM’s cloud computing data centers to process, thus speeding up the amount of time it takes to detect equipment bugs to near real-time. To know more about this project in detail visit Apple's official blog post.