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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A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker
Key Features:
Perform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learnUse the different features and capabilities of SageMaker to automate relevant ML processes
Book 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.
What You Will Learn:
Train and deploy NLP, time series forecasting, and computer vision models to solve different business problemsPush the limits of customization in SageMaker using custom container imagesUse AutoML capabilities with SageMaker Autopilot to create high-quality modelsWork with effective data analysis and preparation techniquesExplore solutions for debugging and managing ML experiments and deploymentsDeal with bias detection and ML explainability requirements using SageMaker ClarifyAutomate intermediate and complex deployments and workflows using a variety of solutions
Who this book is 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.
Publisher : Packt Publishing (October 22, 2021)
Language : English
Paperback : 762 pages
ISBN-10 : 1800567030
ISBN-13 : 978-1800567030
Item Weight : 2.86 pounds
Dimensions : 9.25 x 7.5 x 1.56 inches
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9 reviews for 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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IK –
Excellent for beginners in ML!
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 Customer –
You will need it for serious data science!
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 didnât 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!
Karan –
Amazing guide to give a holistic experience to learn and practice ML concepts on AWS SageMaker
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.
Jamby Jambalos –
A comprehensive handbook of how to make the most out of SageMaker
Amazon Sagemaker (and ML in general) can be intimidating to study. While the docs of AWS are helpful, it does not do much in helping you connect ML concepts with each Sagemaker feature. This book fills that gap by combing through features of Sagemaker and explaining the what (the feature does), the when (to use), the how (to use in Sagemaker), and the why (do we need to do this and what happens if we don’t).Even if the reader does not have in-depth knowledge of ML concepts, he/she can use this book to get started right away. The book teaches ML concepts as it uses Sagemaker features. As a recipe book, it has dozens of recipes to teach (and also show) these features. What I like about this book is that it also explains the “why do we need to do this” behind the feature, something a lot of books in ML have skimped on.I recommend this book to any developer, data scientist or ML enthusiast who would want to learn and use one of the richest ML services out there, SageMaker.
Jerrod Estell –
Great Utility
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.
Shikhar Shah –
Great Resource on SageMaker
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.
Retired Engineer –
Good Hands-on Guide to learning ML with SageMaker
This is a great book for learning hands-on ML from the start. Also good support from publisher and the author. I had an issue with one of the experiments, got a swift email reply with directly relevant information to my issue.So far, not quite 1/2 way through the book, and my AWS costs are just a few dollars. Helpful pointers throughout book on how to control costs.
Gianina –
One of the best books on machine learning on AWS
This book is a great guide for learning and mastering Amazon SageMaker. This book covers a lot of intermediate and advanced solutions on ML model training and deployment where other resources fall short on.The book dives deep into the details by providing clear explanations and diagrams for every step in each of the hands-on examples. Definitely one of the best books I’ve read on machine learning.
Sarah –
Provides hands-on and practical steps on learning machine learning on Amazon SageMaker!