num_pages: 400
description: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan Sara Robinson and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.The authors three Google Cloud engineers describe 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 most appropriate remedy for your situation.Youll learn how to:Identify and mitigate common challenges when training evaluating and deploying ML modelsRepresent data for different ML model types including embeddings feature crosses and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints distribution strategy and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure that models are treating users fairly
language: English
title: Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
publisher: O'Reilly Media