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Amazon SageMaker Best Practices
Amazon SageMaker Best Practices

Amazon SageMaker Best Practices : Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

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Profile Icon Muppala Profile Icon DeFauw Profile Icon Eigenbrode
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$39.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (8 Ratings)
eBook Sep 2021 348 pages 1st Edition
eBook
$39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Muppala Profile Icon DeFauw Profile Icon Eigenbrode
Arrow right icon
$39.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (8 Ratings)
eBook Sep 2021 348 pages 1st Edition
eBook
$39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in production
  • Automate end-to-end machine learning workflows with Amazon SageMaker and related AWS
  • Design, architect, and operate machine learning workloads in the AWS Cloud

Description

Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows.

Who is this book for?

This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.

What you will learn

  • Perform data bias detection with AWS Data Wrangler and SageMaker Clarify
  • Speed up data processing with SageMaker Feature Store
  • Overcome labeling bias with SageMaker Ground Truth
  • Improve training time with the monitoring and profiling capabilities of SageMaker Debugger
  • Address the challenge of model deployment automation with CI/CD using the SageMaker model registry
  • Explore SageMaker Neo for model optimization
  • Implement data and model quality monitoring with Amazon Model Monitor
  • Improve training time and reduce costs with SageMaker data and model parallelism

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 24, 2021
Length: 348 pages
Edition : 1st
Language : English
ISBN-13 : 9781801077767
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What do you get with eBook?

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Product Details

Publication date : Sep 24, 2021
Length: 348 pages
Edition : 1st
Language : English
ISBN-13 : 9781801077767
Category :
Languages :

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Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(8 Ratings)
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J. Wu Dec 18, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a Data Scientist I have done many ML projects in the Jupyter Notebook and I have always been intrigued about AWS SageMaker. The book gave a great overall view of what Safemaker does. This is what I learned from the book: that Sagemaker is not magic. For mature algorithms, it has prebuilt scripts. For more customized projects you need to write your own code. The main point of SafeMaker is to automate and scale. It could potentially automate labor intense labeling. It could facilitate ETL, the whole ML pipeline and scale to big data. Regarding how to do so, this book provides examples of good practices. Overall this book is definitely valuable for someone who is able to dive into AWS SageMaker.
Amazon Verified review Amazon
Wesley Pasfield Sep 24, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Overall this is an excellent and practical overview of the full breadth of the SageMaker platform, I’d recommend this to anyone who is using SageMaker on AWS for full end-to-end machine learning workflows. There are an overwhelming number of products within the SageMaker platform, and this book does a great job of clearly explaining the benefit of each product, and placing it within the broader AWS ecosystem. Often with machine learning platforms and tutorials there’s an over-emphasis on the modeling portion of the process, but this book covers the full machine learning lifecycle including model management, versioning and deployment, and I really appreciated the book’s practical focus.I especially enjoyed the focus on integration with other AWS services, and notably sections with detail on CloudFormation orchestration (particularly the setting up Data Science environments chapter), and would have liked to see more CloudFormation focus in the deployment section. It also would have been nice to see the pros and cons of some of the new SageMaker features vs. their prior AWS service predecessors to better understand the value proposition (ex. Online Feature Store vs. DynamoDB). I thought the comparison of Model Registry options (SageMaker, AWS Custom, and Open Source) was especially strong and a helpful given the vast number of architecture decisions that are possible with AWS
Amazon Verified review Amazon
laura Feb 21, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book contains a detailed overview of the full machine learning lifecycle. Inside you will learn how to process and prepare data, use big data pipelines, and run A/B tests. What is unique and great about the book is its use of example code files/cloudformation provided on GitHub, which allows the reader to follow each chapter and test the features described throughout the book.To get the most out of this book, the reader needs to have a working knowledge of Amazon SageMaker and experience with the AWS console.I particularly enjoyed the fact that the AWS Well Architected Framework is taken into account by including an overview of the AWS services necessary to stay within the well architected guidelines.
Amazon Verified review Amazon
Roger Feb 07, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very comprehensive book about the AWS ML services.For me, the most helpful section was how to operationalize the ML workflow using built-in AWS services as SageMaker Pipelines.For my use case -- ML for Healthcare -- it is being a great resource for automate data collection and data preparation, model building, evaluation, registering and deploy.I have read some books in this matter over the last years, and I can surely recommend this book either for Data Scientists, ML or DevOps Engineers as a choice to go.
Amazon Verified review Amazon
Jim Oct 28, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a comprehensive book on applying Amazon SageMaker to the whole lifecycle of a machine learning process. I highly recommend it to anyone who uses Amazon SageMaker to develop machine learning applications, especially the systems with large-scale datasets. The book provides many advices with examples on how to address data science challenges with large-scale datasets such as data pre-processing and preparation at scale, model training at scale using big data pipelines. In addition, the book also shows how to integrate machine learning pipelines with MLOps principles to automate the development of a secure and performant machine learning application system.
Amazon Verified review Amazon
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