Machine learning design patterns : solutions to common challenges in data preparation, model building, and MLOps / Valliappa Lakshmanan, Sara Robinson, and Michael Munn.

Format:

Book

Author/Creator:

Lakshmanan, Valliappa, author.

Robinson, Sara, author.

Munn, Michael, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (405 pages)

Edition:

1st edition

Place of Publication:

Beijing : O'Reilly, [2021]

System Details:

text file

Summary:

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

Contents:

The need for machine learning design patterns

Data representation design patterns

Problem representation design patterns

Model training patterns

Design patterns for resilient serving

Reproducibility design patterns

Responsible AI

Connected patterns.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781098115739

1098115732

9781098115753

1098115759

9781098115777

1098115775

OCLC:

1202816431