Machine Learning with Amazon SageMaker Cookbook

Machine Learning with Amazon SageMaker Cookbook

eBook Details:

  • Paperback: 762 pages
  • Publisher: WOW! eBook (October 29, 2021)
  • Language: English
  • ISBN-10: 1800567030
  • ISBN-13: 978-1800567030

eBook Description:

Machine Learning with Amazon SageMaker Cookbook: A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this Machine Learning with Amazon SageMaker Cookbook book, you’ll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.

This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You’ll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You’ll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You’ll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you’ll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.

  • Train and deploy NLP, time series forecasting, and computer vision models to solve different business problems
  • Push the limits of customization in SageMaker using custom container images
  • Use AutoML capabilities with SageMaker Autopilot to create high-quality models
  • Work with effective data analysis and preparation techniques
  • Explore solutions for debugging and managing ML experiments and deployments
  • Deal with bias detection and ML explainability requirements using SageMaker Clarify
  • Automate intermediate and complex deployments and workflows using a variety of solutions

By the end of this Machine Learning with Amazon SageMaker Cookbook book, you’ll be able to combine the different solutions you’ve learned as building blocks to solve real-world ML problems.

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