Machine Learning, IoT, and Blockchain in AWS

Emerging technology such as Machine Learning (ML), Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) started as experiments by a handful of technology companies. Over the years, major technology companies, including Amazon, Google, Facebook, and Apple, have driven exponential growth by utilizing the latest emerging technology and staying ahead of the competition. With the cloud, emerging technologies have become accessible to everyone. That is another reason why organizations are rushing to adopt the cloud as it opens the door for innovation with tested technology by industry leaders like Amazon, Microsoft, and Google through their cloud platforms.

Today, the most prominent emerging technologies becoming mainstream are ML and AI. IoT is fueling industry revolutions with smart factories, and autonomous cars and spaces. Blockchain has seen tremendous growth recently, with a boom in cryptocurrencies and temper...

What is AI/ML?

ML is a type of computer technology that allows software to improve its performance automatically by learning from data without being explicitly programmed. It is a way of teaching computers to recognize patterns and make predictions based on examples. In simple terms, ML is a way for computers to learn from data and make predictions or decisions. There are several types of ML, each with its unique characteristics and use cases. The main types of ML include:

• Supervised Learning: Supervised learning is the most widespread form of ML, involving training a model on a labeled dataset to predict the output for new, unseen data. Linear regression, logistic regression, and decision trees are some examples of supervised learning algorithms.
• Unsupervised Learning: Unsupervised learning, on the other hand, does not use labeled data and instead discovers patterns and structures in the input data. Examples of unsupervised learning algorithms include clustering...

AI/ML in AWS

In recent years, ML has rapidly transitioned from a cutting-edge technology to a mainstream one; however, there is still a long way to go before ML is embedded everywhere in our lives. In the past, ML was primarily accessible to a select group of large tech companies and academic researchers. But with the advent of cloud computing, the resources required to work with ML, such as computing power and data, have become more widely available, enabling a wider range of organizations to utilize and benefit from ML technology.

ML has become an essential technology for many industries, and AWS is at the forefront of providing ML services to its customers. Some of the key trends in ML using AWS include:

• Serverless ML: AWS is making it easier to build, train, and deploy ML models without the need to manage servers. With services like Amazon SageMaker, customers can build and train models using managed Jupyter Notebook and then deploy them to a serverless endpoint...

Building ML best practices with MLOps

MLOps are the practices and tools used to manage the full lifecycle of ML models, from development to deployment and maintenance. The goal of MLOps is to make deploying ML models to production as seamless and efficient as possible.

Managing an ML application in production requires a robust MLOps pipeline to ensure that the model is continuously updated and relevant as new data becomes available. MLOps helps automate the building, testing, and deploying of ML models. It manages the data and resources used to train and evaluate models, apply mechanisms to monitor and maintain deployed models to detect and address drift, data quality issues, and bias, and finally enables communication and collaboration between data scientists, engineers, and other stakeholders.

The first step in implementing MLOps in AWS is clearly defining the ML workflow, including the data ingestion, pre-processing, model training, and deployment stages. The following...

What is IoT?

IoT stands for “Internet of Things,” and it refers to the idea of connecting everyday devices to the internet so that they can share data and be controlled remotely. A simple example of this would be a smart thermostat in your home. A smart thermostat is a device you can control from your phone; it learns your temperature preferences and can even detect when you’re away and adjust the temperature accordingly to save energy.

So, instead of manually adjusting the temperature, you can control it remotely using your phone or voice commands. This is just one example of the many ways that IoT can make our lives more convenient and efficient. Another example is a smart fridge, which can keep track of your groceries and alert you when you’re running low on certain items or even order them for you automatically.

IoT refers to the interconnectedness of everyday physical objects, such as devices, vehicles, and buildings, to the internet through...

Building IoT applications in AWS

AWS IoT is a platform that allows you to connect, monitor, and control millions of IoT devices. It provides services that allow you to easily and securely collect, store, and analyze data from IoT devices. Let’s understand AWS IoT services by looking at the following architecture diagram.

Figure 12.4: AWS IoT Services

As shown above, the process of connecting IoT devices to AWS typically starts with connecting the devices to AWS IoT Core. This can be done using different protocols such as MQTT, HTTP, and WebSocket. Once connected, the devices send data to the IoT Core service, which then securely transmits it to the IoT Message Broker using the Device Gateway. The IoT Rules Engine filters and processes the data, which can then be sent to other AWS services for storage, analysis, and visualization. Even when the devices are offline, the IoT Shadow service can be used to track their status. This architecture allows for real-time data...

Best practices to build AWS IoT applications

When building an IoT application on AWS, you should keep the following best practices in mind:

• Secure your devices: Ensure that all your devices are correctly configured and have the latest security updates. Use AWS IoT Device Defender to monitor and secure your devices against potential security threats.
• Use MQTT or HTTPS for communication: These protocols are designed for low-bandwidth, low-power devices and are well suited for IoT applications.
• Use AWS IoT Analytics to process and analyze your data: This service provides tools for cleaning, filtering, and transforming IoT data before it is analyzed.
• Store your data in the right place: Depending on your use case, you may want to store your data in a time-series database like Amazon Timestream or a data lake like Amazon S3.
• Use AWS IoT Greengrass for edge computing: With Greengrass, you can run AWS Lambda functions on your devices, allowing you to process...

Blockchain in AWS

Blockchain is a digital ledger that is used to record transactions across a network of computers. It is a decentralized system, which means that it is not controlled by any single entity, and it is highly secure because it uses cryptography to secure and validate transactions and keep them private. Each block in the chain contains a record of multiple transactions, and after a block has been added to the chain, it cannot be altered or deleted. This makes blockchain technology useful for a variety of applications, including financial transactions, supply chain management, and secure record-keeping.

Blockchain allows multiple parties to securely and transparently record and share information without a central authority. The most well-known use of blockchain technology is in creating digital currencies like Bitcoin, but it can be used for a wide range of applications, such as supply chain management, smart contracts, and voting systems. Blockchain technology is...

Quantum computing with AWS Braket

Quantum computing is a computing method that employs quantum-mechanical phenomena to execute data operations. In quantum computing, data is expressed as qubits, or quantum bits, which can simultaneously exist in multiple states.

This unique attribute empowers quantum computers to perform specific calculations much more rapidly than classical computers. Despite its potential, this technology is still in its infancy and necessitates specialized hardware and expertise to operate. Some of the key use cases where quantum computing can be very efficient are:

• Drug discovery and materials science: Quantum computing can be used to simulate complex chemical and biological systems, which can help in the discovery of new drugs and materials.
• Financial modeling: Quantum computing can solve complex financial problems such as portfolio optimization, option pricing, and risk analysis.
• ML: Quantum computing can be used to develop new algorithms...

Generative AI

With the launch of ChatGPT (Generative Pre-trained Transformer), generative AI has become the talk of the town. It has opened endless possibilities for revolutionizing the way we work today. This revolution is comparable to the innovation brought about by computers, and how the world moved from typewriters to shiny new computers, which made things more efficient. ChatGPT is just one dimension that shows the world the art of possibility and brings much-needed innovation that the world has been waiting for for a long time. Over the last two decades, you might have wondered who can challenge the position of Google in the AI market, especially Google Search. But, as you know, there is always a disrupter; if you don’t innovate fast enough, someone else will do it. ChatGPT has brought that innovation to the hands of everyone.

Let’s first understand what generative AI is. Generative AI uses AI algorithms to create new content that resembles content from a...

Summary

Organizations must drive innovation and stay agile by using emerging technologies to stay ahead of the competition. With cloud providers like AWS, these technologies are easily accessible for you to experiment with and add to your use case.

In this chapter, you began by learning about ML and AI. You learned how AWS services help build an end-to-end ML pipeline, taking an ML workload from ideation to production. You learned about three layers of AWS AI/ML services, starting with the ML infrastructure provided by AWS to train your model.

After that, you learned about Amazon SageMaker, which is at the center of the AWS ML tech stack to build, train, deploy, tune, and monitor ML models. Next, you learned about the top stack where AWS AI services reside, providing pre-trained models that can be used with simple API calls without any knowledge of ML. This AI service is available to address multiple use cases involving vision, speech, chatbots, forecasting, and recommendations...