Machine Learning Design Patterns

Discover best practices and solutions to common problems in machine learning with this book. The authors, Google Cloud engineers, offer tried-and-proven methods for engineers to tackle issues that arise while working with ML. In Machine Learning Design Patterns, they describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Choose the most appropriate remedy for your situation with each pattern's potential solutions and recommendations. Learn how to identify and mitigate challenges, represent data for different ML model types, choose the right model type for specific problems, build a robust training loop, deploy scalable ML systems, and interpret model predictions for stakeholders.