You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781801070157

Length 326 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Michael Hsieh

View More author details

Table of Contents (16) Chapters

Preface

1. Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio

2. Chapter 1: Machine Learning and Its Life Cycle in the Cloud FREE CHAPTER

3. Chapter 2: Introducing Amazon SageMaker Studio

4. Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

5. Chapter 3: Data Preparation with SageMaker Data Wrangler

6. Chapter 4: Building a Feature Repository with SageMaker Feature Store

7. Chapter 5: Building and Training ML Models with SageMaker Studio IDE

8. Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify

9. Chapter 7: Hosting ML Models in the Cloud: Best Practices

10. Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot

11. Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

12. Chapter 9: Training ML Models at Scale in SageMaker Studio

13. Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor

14. Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry

15. Other Books You May Enjoy

Using the SageMaker Python SDK

SageMaker Studio is more than just a place to run codes in notebooks. Yes, SageMaker Studio is a great place to start coding and training ML models in elastic notebooks, but there are so many more capabilities, as we discussed in the Introducing SageMaker Studio and its components section in this chapter.

There are two main ways to communicate and work with SageMaker features. One is through the components that have a UI frontend, such as SageMaker Data Wrangler; the other is through a Software Development Kit (SDK). The SDK enables developers to interact with the world of Amazon SageMaker beyond the interface. You can access SageMaker's scalable, built-in algorithms for your data. You can programmatically run SageMaker Autopilot jobs. If you develop your deep learning models with TensorFlow, PyTorch, or MXNet, you can use the SDK to interact with the SageMaker compute infrastructure for training, processing, and hosting models for them. You...

The rest of the chapter is locked

You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Using the SageMaker Python SDK

Authors (1)

Personalised recommendations for you

You're reading from Getting Started with Amazon SageMaker Studio Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

Table of Contents (16) Chapters

Using the SageMaker Python SDK

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you