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Machine Learning Design Patterns

A catalog of thirty reusable design patterns that provide proven solutions to common challenges in data preparation, model building, and MLOps for machine learning practitioners.

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What it’s about

For data scientists, data engineers, and ML engineers who already know the basics of machine learning, this book provides the crucial next step: learning the hard-earned best practices for building robust, scalable, and maintainable production ML systems. Moving beyond the 'what' and 'how' of algorithms, this book focuses on the 'why' behind the solutions that experienced practitioners use to solve recurring problems in data representation, problem framing, model training, resilient serving, and reproducibility. It offers a catalog of 30 practical design patterns, complete with code examples and trade-off analyses, giving you and your team a shared vocabulary and a toolkit of proven strategies to build high-quality ML systems that deliver real-world value.

The through-line

Who it’s for
The reader is a data scientist, ML engineer, or data engineer who knows the basics of ML but struggles to build robust, scalable, and maintainable production ML systems. They want to move beyond academic exercises and implement effective solutions for real-world business problems.
The problem
They repeatedly face challenges with data quality, training-serving skew, model degradation, reproducibility, and scalability when trying to move ML models from prototype to production. They feel frustrated and inefficient, constantly 'reinventing the wheel' for common problems. They lack the confidence and vocabulary to apply proven solutions and communicate their design choices effectively.
The plan
  1. Learn to represent data effectively using patterns like Embeddings and Feature Crosses.
  2. Frame your ML problem correctly with patterns like Reframing and Rebalancing.
  3. Master the training process with patterns for Transfer Learning, Distribution Strategy, and Hyperparameter Tuning.
  4. Build resilient and reproducible systems using patterns for serving, workflow pipelines, and feature stores.
  5. Implement responsible AI by using patterns for explainability, fairness, and benchmarking.
The payoff
The reader can confidently design, build, and deploy robust, scalable, and maintainable ML systems. · They have a shared vocabulary to communicate design decisions with their team. · They build higher-quality models faster, avoiding common pitfalls and rework.

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