Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.
This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.
By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.
publication date
2021-10-29
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 SageMaker Autopilot to create high-quality models
Work with effective data analysis and preparation techniques
Explore solutions for debugging and managing ML experiments and deployments
Deal with bias detection and ML explainability requirements using SageMaker Clarify
Automate intermediate and complex deployments and workflows using a variety of solutions
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
approach
You will use a recipe-based and step-by-step approach in this book to deal with key challenges faced in machine learning experiments and projects. You will generate and work with synthetic datasets to ensure that the NLP, computer vision and time series forecasting experiments performed in this book are straightforward, simple, and easy to understand. The majority of the recipes in this book make use of the Python programming language and a certain portion focusing on customization make use of R to demonstrate the flexibility of using SageMaker when dealing with machine learning and deep learning requirements.
audience
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 you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
short description
Explore the capabilities of Amazon SageMaker to effectively train, deploy, and manage machine learning and deep learning models in AWS with complete and thoroughly explained hands-on examples. With 80 proven recipes, this step-by-step guide will help you build the skills necessary to contribute to real-life ML requirements and projects.
subtitle
80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
keywords
Machine learning book, Natural language processing, TensorFlow, PyTorch, deep learning, data processing, data analyzing, Amazon Athena, Kubeflow
Product ISBN
9781800567030
