What this book covers
Chapter 1, Introducing Amazon SageMaker, provides an overview of Amazon SageMaker, what its capabilities are, and how it helps solve many pain points faced by machine learning projects today.
Chapter 2, Handling Data Preparation Techniques, discusses data preparation options. Although it isn't the core subject of the book, data preparation is a key topic in machine learning, and it should be covered at a high level.
Chapter 3, AutoML with Amazon SageMaker AutoPilot, shows how to build, train, and optimize machine learning models automatically with Amazon SageMaker AutoPilot.
Chapter 4, Training Machine Learning Models, shows how to build and train models using the collection of statistical machine learning algorithms built into Amazon SageMaker.
Chapter 5, Training Computer Vision Models, shows how to build and train models using the collection of computer vision algorithms built into Amazon SageMaker.
Chapter 6, Training Natural Language Processing Models, shows how to build and train models using the collection of natural language processing algorithms built into Amazon SageMaker.
Chapter 7, Extending Machine Learning Services Using Built-In Frameworks, shows how to build and train machine learning models using the collection of built-in open source frameworks in Amazon SageMaker.
Chapter 8, Using Your Algorithms and Code, shows how to build and train machine learning models using their own code on Amazon SageMaker, for example, R or custom Python.
Chapter 9, Scaling Your Training Jobs, shows how to distribute training jobs to many managed instances, using either built-in algorithms or built-in frameworks.
Chapter 10, Advanced Training Techniques, shows how to leverage advanced training in Amazon SageMaker.
Chapter 11, Deploying Machine Learning Models, shows how to deploy machine learning models in a variety of configurations.
Chapter 12, Automating Machine Learning Workflows, shows how to automate the deployment of machine learning models on Amazon SageMaker.
Chapter 13, Optimizing Cost and Performance, shows how to optimize model deployments, both from an infrastructure perspective and from a cost perspective.
