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Key benefits

Build, train, and deploy machine learning models quickly using Amazon SageMaker

Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques

Improve productivity by training and fine-tuning machine learning models in production

Description

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.

Who is this book for?

This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

What you will learn

Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS)

Become well-versed with data annotation and preparation techniques

Use AutoML features to build and train machine learning models with AutoPilot

Create models using built-in algorithms and frameworks and your own code

Train computer vision and NLP models using real-world examples

Cover training techniques for scaling, model optimization, model debugging, and cost optimization

Automate deployment tasks in a variety of configurations using SDK and several automation tools

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

Gokulaa Dec 30, 2020

Great book to start your ML journey on AWS

Amazon Verified review

Polgár Oct 15, 2020

Si usas Amazon SageMaker, debes saber quién es Julien SImon.Por lo tanto..... Debes de comprar el libro, tú sabes a lo que me refiero.Si no sabes quién es es el Autor y quieres usar SageMaker.¡Con más razón debes de comprar el libro!

RajN Dec 17, 2020

This is a comprehensive book for a data scientist looking to use the AWS ecosystem for machine learning with a focus on Sagemaker. I like the way it is organized which is practical and matches a typical life-cycle of a project -- Setup environment both local and cloud.- Prepare the data using Sagemaker Groundtruth, leveraging other AWS compute services.- Build and Train models using Auto pilot, supervised and un-supervised learning, Computer Vision Models, NLP models and other advanced training techniques.- Operationalizing the model to production, batch and interface pipelines, monitoring predictions, deploying to container services and automating workflows.The author is very knowledgeable and provides several practical examples, code and best practices. I highly recommend this if you want to use AWS Sagemaker.

Science Geek Aug 11, 2021

I usually avoid books on AWS I have been burned too many times. They usually read like press releases for AWS (for instance, regurgitating the "Six Advantages of Cloud Computing" from the AWS documentation), are not practical, or are out-of-date by the time ink hits paper.This book is a refreshing exception, providing a clear beginner-friendly overview of using the Sagemaker tools for machine learning at AWS. It has up-to-date references on all steps of the pipeline from annotating data to deploying a trained model to production. It includes coverage of boto3 (Python SDK), the Sagemaker SDK, and how to set these up on your local machine. It includes discussion of running your code online in notebook instances or in Sagemaker Studio.I recommend knowing the basics of AWS before reading this book. E.g., the kind of thing you would learn in an 'AWS Practitioner' course online: what is S3, how do you manage your account, what is an IAM role, that kind of thing. However, one thing that is nice is that for every new AWS concept it requires as background, it provides a link so you can learn about it or refresh your memory.This is the first AWS book that I've really liked -- well done!

Famille Sekher Sep 17, 2020

Pas encore termine la lecture, mais la bonne structure rends le livre facile a lire et appréhender.

Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists

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