English | 2021 | ISBN: 1800567030 | 763 pages | True PDF EPUB | 61.2 MB

A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with SageMaker

Key Features

Perform ML expents 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

SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML expents. In this book, you'll use the different capabilities and features of 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 expents and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, 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 SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Expents to debug, manage, and monitor multiple ML expents 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.

What you will learn

Train and deploy NLP, 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 expents 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

Who this book is for

This book is for developers, data scientists, and machine learning practitioners interested in using 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.

Table of Contents

Getting Started with Machine Learning Using SageMaker

Building and Using your own Algorithm Container Image

Using Machine Learning and Deep Learning Frameworks with SageMaker

Preparing, Processing, and Analyzing the Data

Effectively Managing Machine Learning Expents

Automated Machine Learning in SageMaker

Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor

Solving NLP, Image Classification, and -Series Forecasting Problems with Built-in Algorithms

Managing Machine Learning Workflows and Deployments