Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key Features * 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 services * Design, architect, and operate machine learning workloads in the AWS Cloud Book 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 and addressing 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 services 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. What you will learn * Perform data bias detection with AWS Data Wrangler and Amazon SageMaker Clarify * Speed up data processing with Amazon SageMaker Feature Store * Reduce training costs with Amazon SageMaker Managed Spot * Stream large data sets with Amazon SageMaker Pipe mode * Improve training time with the monitoring and profiling capabilities of Amazon SageMaker Debugger * Manage registered models using Amazon SageMaker Model Registry * Explore SageMaker Neo for model optimization * Implement data and model quality monitoring with Amazon Model Monitor Who This Book Is 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 services related to data, security, and monitoring will help you make the most out of the book.

Book Information
ISBN 9781801070522
Author Sireesha Muppala
Format Paperback
Page Count 243
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited
Weight(grams) 75g