Amazon SageMaker Best Practices
Going beyond the basics, Amazon SageMaker Best Practices provides end-to-end coverage of the service capabilities that the platform offers for building and automating machine learning workloads to address data science challenges. With this book, you'll discover tips to train, deploy, and monitor your machine learning solutions efficiently.
Offered by
Difficulty Level
Intermediate
Completion Time
11h36m
Language
English
About Book
Who Is This Book 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 related to data, security, and monitoring will help you make the most of the book.
Amazon SageMaker Best Practices
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 11h36m total length
Amazon SageMaker Overview
Data Science Environments
Data Labeling with Amazon SageMaker Ground Truth
Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
Centralized Feature Repository with Amazon SageMaker Feature Store
Training and Tuning at Scale
Profile Training Jobs with Amazon SageMaker Debugger
Managing Models at Scale Using a Model Registry
Updating Production Models Using Amazon SageMaker Endpoint Production Variants
Optimizing Model Hosting and Inference Costs
Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
Machine Learning Automated Workflows
Well-Architected Machine Learning with Amazon SageMaker
Managing SageMaker Features Across Accounts
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