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The ml-design-patterns repository contains the source code and examples that accompany the book “Machine Learning Design Patterns,” providing practical implementations of reusable solutions for common challenges in machine learning systems. It organizes patterns into categories such as data representation, problem framing, and model training, helping practitioners understand how to structure ML pipelines effectively. The repository includes implementations of techniques like feature hashing, embeddings, feature crosses, and multimodal inputs, which are essential for handling diverse data types. It also covers strategies for improving model performance and robustness, including transfer learning, checkpointing, ensemble methods, and rebalancing techniques for imbalanced datasets. By focusing on design patterns rather than isolated algorithms, the project helps developers build scalable, maintainable, and production-ready ML systems.
Features
- Collection of reusable machine learning design patterns
- Coverage of data, problem, and training pattern categories
- Examples including embeddings, feature crosses, and ensembles
- Techniques for transfer learning and model checkpointing
- Focus on scalable and production-ready ML architecture
- Companion code aligned with structured learning material
Project Samples
