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Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode

E-book
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 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 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.
  • 1. Amazon SageMaker Overview
  • 2. Data Science Environments
  • 3. Data Labeling with Amazon SageMaker Ground Truth
  • 4. Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing
  • 5. Centralized Feature Repository with Amazon SageMaker Feature Store
  • 6. Training and Tuning at Scale
  • 7. Profile Training Jobs with Amazon SageMaker Debugger
  • 8. Managing Models at Scale Using a Model Registry
  • 9. Updating Production Models Using Amazon SageMaker Endpoint Production Variants
  • 10. Optimizing Model Hosting and Inference Costs
  • 11. Monitoring Production Models with Amazon SageMaker Model Monitor and Clarify
  • 12. Machine Learning Automated Workflows
  • 13. Well-Architected Machine Learning with Amazon SageMaker
  • 14. Managing SageMaker Features Across Accounts
  • Titel: Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
  • Autor: Sireesha Muppala, Randy DeFauw, Shelbee Eigenbrode
  • Originaler Titel: Amazon SageMaker Best Practices. Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
  • ISBN: 9781801077767, 9781801077767
  • Veröffentlichungsdatum: 2021-09-24
  • Format: E-book
  • Artikelkennung: e_2a96
  • Verleger: Packt Publishing