Amazon SageMaker Best Practices

Amazon SageMaker Best Practices

eBook Details:

  • Paperback: 348 pages
  • Publisher: WOW! eBook (September 24, 2021)
  • Language: English
  • ISBN-10: 1801070520
  • ISBN-13: 978-1801070522

eBook Description:

Amazon SageMaker Best Practices: 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

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.

  • 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 time with the monitoring and profiling capabilities of SageMaker Debugger
  • Address the challenge of model deployment automation with CI/CD using the SageMaker model registry
  • Explore SageMaker Neo for model optimization
  • Implement data and model quality monitoring with Amazon Model Monitor
  • Improve training time and reduce costs with SageMaker data and model parallelism

By the end of the Amazon SageMaker Best Practices book, you’ll confidently be able to apply Amazon SageMaker’s wide range of capabilities to the full spectrum of machine learning workflows.

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