Preface

Amazon SageMaker is a fully managed machine learning (ML) service that aims to help data scientists and ML practitioners manage ML experiments. In this book, you will use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML requirements.

This step-by-step guide has 80 proven recipes designed to give you the hands-on experience needed to contribute to real-world ML experiments and projects. The book covers different algorithms and techniques for training and deploying NLP, time series forecasting, and computer vision models to solve various ML problems. You will explore various solutions when working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. In addition to these, you will learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. You will also have a better understanding of how SageMaker Feature Store, SageMaker Autopilot, and SageMaker Pipelines can solve the different needs of data science teams.

By the end of this book, you will be able to combine the different solutions you have learned as building blocks to solve real-world ML requirements.