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

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