Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of 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 SageMaker and related AWS

Design, architect, and operate machine learning workloads in the AWS Cloud

Book Description

The book bs with a high-level overview of 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 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 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 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 SageMaker Clarify

Speed up data processing with SageMaker Feature Store

Overcome labeling bias with SageMaker Ground Truth

Improve training with the monitoring and profiling capabilities of SageMaker Debugger

Address the challenge of model deployment automation with D using the SageMaker model registry

Explore SageMaker Neo for model optimization

Implement data and model quality monitoring with Model Monitor

Improve training and reduce costs with SageMaker data and model parallelism

Who this book is for

This book is for expert data scientists responsible for building machine learning applications using SageMaker. Working knowledge of 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.

Table of Contents

SageMaker Overview

Data Science Environments

Data Labeling with SageMaker Ground Truth

Data Preparation at Scale Using SageMaker Data Wrangler and Processing

Centralized Feature Repository with SageMaker Feature Store

Training and Tuning at Scale

Profile Training Jobs with SageMaker Debugger

Managing Models at Scale Using a Model Registry

Updating Production Models Using SageMaker Endpoint Production Variants

Optimizing Model Hosting and Inference Costs

Monitoring Production Models with SageMaker Model Monitor and Clarify

Machine Learning Automated Workflows

Well-Architected Machine Learning with SageMaker

Managing SageMaker Features Across Accounts



DOWNLOAD
uploadgig.com


rapidgator.net


nitro.download