Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines: Fregly, Chris, Barth, Antje: 9781492079392: Amazon.com: Books

Chapter 1 provides an overview of the broad and deep Amazon AI and ML stack, an enormously powerful and diverse set of services, open source libraries, and infrastructure to use for data science projects of any complexity and scale. Chapter 2 describes how to apply the Amazon AI and ML stack to real-world use cases for recommendations, computer vision, fraud detection, natural language understanding (NLU), conversational devices, cognitive search, customer support, industrial predictive maintenance, home automation, Internet of Things (IoT), healthcare, and quantum computing. Chapter 3 demonstrates how to use AutoML to implement a specific subset of these use cases with SageMaker Autopilot. Chapter 11 demonstrates real-time ML, anomaly detection, and streaming analytics on real-time data streams with Amazon Kinesis and Apache Kafka. Chapter 12 presents a comprehensive set of security best practices for data science projects and workflows, including IAM, authentication, authorization, network isolation, data encryption at rest, post-quantum network encryption in transit, governance, and auditability.