You're reading from Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Product type Paperback

Published in Oct 2021

Publisher Packt

ISBN-13 9781800567030

Length 762 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Joshua Arvin Lat

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Table of Contents (11) Chapters

Preface

1. Chapter 1: Getting Started with Machine Learning Using Amazon SageMaker

2. Chapter 2: Building and Using Your Own Algorithm Container Image FREE CHAPTER

3. Chapter 3: Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker

4. Chapter 4: Preparing, Processing, and Analyzing the Data

5. Chapter 5: Effectively Managing Machine Learning Experiments

6. Chapter 6: Automated Machine Learning in Amazon SageMaker

7. Chapter 7: Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor

8. Chapter 8: Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms

9. Chapter 9: Managing Machine Learning Workflows and Deployments

10. Other Books You May Enjoy

Identifying issues with SageMaker Debugger

Amazon SageMaker Debugger is one of the more powerful capabilities of Amazon SageMaker that can help us manage our ML experiments. With SageMaker Debugger, we can automatically detect issues and profile training jobs using Debugger rules. We are then able to eliminate these issues and bottlenecks, which would help improve training time and significantly reduce costs. SageMaker Debugger can also be used to monitor the hardware resource usage of training jobs. This feature can help significantly reduce costs as we are able to profile training jobs, detect issues caused by hardware resource usage early, and optimize training time and resource usage. SageMaker Debugger supports ML frameworks and algorithms such as XGBoost, PyTorch, TensorFlow, and MXNet.

There are several built-in Debugger rules to choose from. These include (but are not limited to) the VanishingGradient, PoorWeightInitialization, ExplodingTensor, DeadRelu, and LossNotDecreasing...

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You're reading from Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Table of Contents (11) Chapters

Identifying issues with SageMaker Debugger

Authors (1)

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You're reading from Machine Learning with Amazon SageMaker Cookbook 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Table of Contents (11) Chapters

Identifying issues with SageMaker Debugger

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