0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Amazon SageMaker Best Practices

You're reading from Amazon SageMaker Best Practices Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801070522

Length 348 pages

Edition 1st Edition

Languages

Python

Tools

Amazon SimpleDB

Concepts

Machine Learning

Authors (3):

Randy DeFauw

Shelbee Eigenbrode

Sireesha Muppala

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Processing Data at Scale

2. Chapter 1: Amazon SageMaker Overview FREE CHAPTER

3. Chapter 2: Data Science Environments

4. Chapter 3: Data Labeling with Amazon SageMaker Ground Truth

5. Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing

6. Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store

7. Section 2: Model Training Challenges

8. Chapter 6: Training and Tuning at Scale

9. Chapter 7: Profile Training Jobs with Amazon SageMaker Debugger

10. Section 3: Manage and Monitor Models

11. Chapter 8: Managing Models at Scale Using a Model Registry

12. Chapter 9: Updating Production Models Using Amazon SageMaker Endpoint Production Variants

13. Chapter 10: Optimizing Model Hosting and Inference Costs

14. Chapter 11: Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify

15. Section 4: Automate and Operationalize Machine Learning

16. Chapter 12: Machine Learning Automated Workflows

17. Chapter 13:Well-Architected Machine Learning with Amazon SageMaker

18. Chapter 14: Managing SageMaker Features across Accounts

19. Other Books You May Enjoy

Section 4: Automate and Operationalize Machine Learning

In this section, we will build automated workflows and MLOps pipelines for end-to-end ML solutions following best practices for security, reliability, performance, and cost optimization.

This section comprises the following chapters:

Chapter 12, Machine Learning Automated Workflows
Chapter 13, Well-Architected Machine Learning with Amazon SageMaker
Chapter 14, Managing SageMaker Features across Accounts

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at ₹800/month. Cancel anytime

Authors (3)

Muppala

Muppala

Sireesha Muppala, PhD is a Principal Enterprise Solutions Architect, AI/ML at Amazon Web Services (AWS). Sireesha holds a PhD in computer science and post-doctorate from the University of Colorado. She is a prolific content creator in the ML space with multiple journal articles, blogs, and public speaking engagements. Sireesha is a co-creator and instructor of the Practical Data Science specialization on Coursera. She is a co-director of Women In Big Data (WiBD), Denver chapter. Sireesha enjoys helping organizations design, architect, and implement ML solutions at scale.

See other products by Muppala

Eigenbrode

Eigenbrode

Shelbee Eigenbrode is a Principal AI and ML Specialist Solutions Architect at AWS. She holds six AWS certifications and has been in technology for 23 years, spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.

See other products by Eigenbrode

DeFauw

DeFauw

Randy DeFauw is a Principal Solution Architect at AWS. He holds an MSEE from the University of Michigan, where his graduate thesis focused on computer vision for autonomous vehicles. He also holds an MBA from Colorado State University. Randy has held a variety of positions in the technology space, ranging from software engineering to product management. He entered the big data space in 2013 and continues to explore that area. He is actively working on projects in the ML space, including reinforcement learning. He has presented at numerous conferences, including GlueCon and Strata, published several blogs and white papers, and contributed many open source projects to GitHub.

See other products by DeFauw

Other recommended products

Related to this chapter

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Learn Amazon SageMaker

Learn Amazon SageMaker

This book will teach you how to move quickly from business questions to machine learning models in production. Using real-world examples implemented with Python and Jupyter notebooks, you'll learn about many the features and APIs of Amazon SageMaker on a wide spectrum of use cases: tabular data, computer vision, and natural language processing.

Aug 2020 16h 20m

Automated Machine Learning

Automated Machine Learning

This guide will help you to explore automated machine learning (AutoML), a rapidly growing subfield of machine learning. You'll learn how you can use AutoML to fully automate the machine learning process even if you're not an expert, and in turn increase your productivity drastically.

Feb 2021 10h 24m

Automated Machine Learning

Automated Machine Learning

This guide will help you to explore automated machine learning (AutoML), a rapidly growing subfield of machine learning. You'll learn how you can use AutoML to fully automate the machine learning process even if you're not an expert, and in turn increase your productivity drastically.

Feb 2021 10h 24m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m