With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more - Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot - Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment - Tie everything together into a repeatable machine learning operations pipeline - Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka - Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more "Wow--this book will help you to bring your data science projects from idea all the way to production. Chris and Antje have covered all of the important concepts and the key AWS services, with plenty of real-world examples to get you started on your data science journey." --Jeff Barr, Vice President & Chief Evangelist, Amazon Web Services "It's very rare to find a book that comprehensively covers the full end-to-end process of model development and deployment! If you're an ML practitioner, this book is a must!" --Ramine Tinati, Managing Director/Chief Data Scientist Applied Intelligence, Accenture "This book is a great resource for building scalable machine learning solutions on AWS cloud. It includes best practices for all aspects of model building, including training, deployment, security, interpretability, and MLOps." --Geeta Chauhan, AI/PyTorch Partner Engineering Head, Facebook AI "The landscape of tools on AWS for data scientists and engineers can be absolutely overwhelming. Chris and Antje have done the community a service by providing a map that practitioners can use to orient themselves, find the tools they need to get the job done and build new systems that bring their ideas to life." --Josh Wills, Author, Advanced Analytics with Spark (O'Reilly) "Successful data science teams know that data science isn't just modeling but needs a disciplined approach to data and production deployment. We have an army of tools for all of these at our disposal in major clouds like AWS. Practitioners will appreciate this comprehensive, practical field guide that demonstrates not just how to apply the tools but which ones to use and when." --Sean Owen, Principal Solutions Architect, Databricks With this practical book, AI and machine learning (ML) practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services (AWS). The Amazon AI and ML stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. * Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more. * Use automated ML (AutoML) to implement a specific subset of use cases with Amazon SageMaker Autopilot. * Dive deep into the complete model development life cycle for a BERT-based natural language processing (NLP) use case including data ingestion and analysis, and more. * Tie everything together into a repeatable ML operations (MLOps) pipeline. * Explore real-time ML, anomaly detection, and streaming analytics on real-time data streams with Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK). * Learn security best practices for data science projects and workflows, including AWS Identity and Access Management (IAM), authentication, authorization, and more. Overview of the Chapters 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 predic