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Machine Learning with Amazon SageMaker Cookbook
Machine Learning with Amazon SageMaker Cookbook

Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

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Profile Icon Joshua Arvin Lat
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€11.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (9 Ratings)
Paperback Oct 2021 762 pages 1st Edition
eBook
€32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €11.99p/m
Arrow left icon
Profile Icon Joshua Arvin Lat
Arrow right icon
€11.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (9 Ratings)
Paperback Oct 2021 762 pages 1st Edition
eBook
€32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €11.99p/m
eBook
€32.99
Paperback
€41.99
Subscription
Free Trial
Renews at €11.99p/m

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Key benefits

  • Perform ML experiments 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

Description

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

Who is this book for?

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

What you will learn

  • Train and deploy NLP, time 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 experiments 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 29, 2021
Length: 762 pages
Edition : 1st
Language : English
ISBN-13 : 9781800567030
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Product Details

Publication date : Oct 29, 2021
Length: 762 pages
Edition : 1st
Language : English
ISBN-13 : 9781800567030
Category :
Languages :
Tools :

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(9 Ratings)
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Karan Jan 16, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book's language is simple, easy to understand, and to follow, and provides a good platform for the data scientist, analysts, machine learning enthusiasts, project managers to have a hands-on experience on the SageMaker.This book gives a great platform using easy language, visual diagrams, practical exercises with step-by-step instructions, and video links for better understanding. The author and publisher both give full support for any doubt, questions, or clarification about the book or the concept.After completing this book, you will have the skillset and expertise for end-to-end usage and deployment of machine learning algorithms on the AWS platform.In short, a complete guide to understanding each and every detail of the AWS SageMaker platform.
Amazon Verified review Amazon
Shikhar Shah Dec 10, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great book to learn SageMaker by doing. It really helped me explore machine learning in cloud computing. The recipes are useful in making sure readers learn by doing.
Amazon Verified review Amazon
Jerrod Estell Dec 10, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is essential for SageMaker users. Each SageMaker algorithm is covered in detail with practical use cases making the book practical to pick up and flip to it for your implementation. 5/5.
Amazon Verified review Amazon
IK Jul 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As someone who hasn't had any experience in machine learning engineering whatsoever, this book was a good introduction on what happens after creating your model. There are currently a lot of references on how to create a model, but there aren't enough references for deploying them, so I am thankful that I got the chance to learn from this book.
Amazon Verified review Amazon
Amazon Customer Dec 14, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Machine Learning with Amazon SageMaker Cookbook is a must-have book for data science/machine learning practitioners. Its content is very comprehensive as it covers ML implementations from easy to intermediate level, ranging from regression/classification to computer vision, NLP, and transfer learning on AWS. Besides, I appreciate the fact that this book was published relatively recently and that its code examples are easily executable based on the latest version of Amazon SageMaker (I am sure you understand the frustration of reading a techie book whose code examples are outdated that you must spend a lot of time on debugging). I also appreciate the fact that there are explanations to literally every line of the codes presented in this book so that I didn’t get confused and lost as I was trying out the codes in my machine. Here, I do have a suggestion to the potential readers: as AWS SageMaker has so many features and functionalities that even if this book has done its best in explaining most of them, I still find it hard to absorb all the concepts and methodologies at once; so I find it helpful to, instead of using this book as a textbook (like you need to understand almost everything to a point that you can pass the exam), use it as a dictionary to your day-to-day DS/ML practice, i.e. you can quickly skim through this book several times to have an idea of what each section of each chapter is about and what problem it teaches to solve, and then when you come across a problem in practice, you can quickly locate the relevant section in the book and attempt the codes directly. Overall, I enjoy this book very much and have kept it in my digital library that I can easily access and reference in my data science job!
Amazon Verified review Amazon
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