9781098115784-1098115783-Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

ISBN-13: 9781098115784
ISBN-10: 1098115783
Edition: 1
Author: Valliappa Lakshmanan, Michael Munn, Sara Robinson
Publication date: 2020
Publisher: O'Reilly Media
Format: Paperback 405 pages
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Book details

ISBN-13: 9781098115784
ISBN-10: 1098115783
Edition: 1
Author: Valliappa Lakshmanan, Michael Munn, Sara Robinson
Publication date: 2020
Publisher: O'Reilly Media
Format: Paperback 405 pages

Summary

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (ISBN-13: 9781098115784 and ISBN-10: 1098115783), written by authors Valliappa Lakshmanan, Michael Munn, Sara Robinson, was published by O'Reilly Media in 2020. With an overall rating of 4.4 stars, it's a notable title among other AI & Machine Learning (Computer Science) books. You can easily purchase or rent Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (Paperback, Used) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $16.83.

Description

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.

You'll learn how to:

  • Identify and mitigate common challenges when training, evaluating, and deploying ML models
  • Represent data for different ML model types, including embeddings, feature crosses, and more
  • Choose the right model type for specific problems
  • Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning
  • Deploy scalable ML systems that you can retrain and update to reflect new data
  • Interpret model predictions for stakeholders and ensure models are treating users fairly

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