A step-by-step problem solution based guide to prepare, build, train, and deploy high-quality machine learning (ML) models with Amazon SageMaker
Key Features
- Perform ML experiments with built-in and custom algorithms in SageMaker
- Explore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learn.
- Use the different features and capabilities of SageMaker to automate relevant ML processes
Book Description
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 when 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.
What you will learn
- Train and deploy NLP, time series forecasting, and computer vision models to solve different business problems
- Push the limits of customization in SageMaker using custom container images
- Use AutoML capabilities with Autopilot to create high-quality models
- Work with effective data analysis and preparation techniques
- Explore solutions for debugging and managing ML experiments and deployment
- Deal with bias detection and ML explainability requirements with SageMaker Clarify
- Automate intermediate and complex deployments and workflows using a variety of solutions
Who This Book Is For
This book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All we need is an AWS account to get things running. Some knowledge in AWS, machine learning, and the Python programming language will help readers grasp the concepts in this book more effectively.
