Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps - Softcover
Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Valliappa (Lak) Lakshmanan is Global Head for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA
"About this title" may belong to another edition of this title.
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