Machine Learning Platform Engineering

Building a machine-learning model is only one part of the job. Getting it trained reproducibly, deployed reliably, monitored in use, and maintained as data changes takes a platform—and a connected set of engineering practices. Machine Learning Platform Engineering takes readers through that broader challenge, from MLOps foundations to the operational demands of modern LLM applications.

Design the platform around the full ML life cycle

The book starts with the work that surrounds a model: data preparation, experimentation, evaluation, deployment, monitoring, and ongoing maintenance. It introduces MLOps principles and platform-building decisions, then lays infrastructure groundwork with Docker, Kubernetes, continuous integration and delivery, Prometheus, and Grafana. The emphasis is on how the pieces fit together across development and production.

Connect the tools that make ML workflows repeatable

As the platform takes shape, the authors explore MLflow for experiment tracking and model management, Feast for feature storage, and Kubeflow Pipelines for orchestration. Later chapters bring in BentoML for model serving and Evidently for data-drift monitoring. These examples make the book particularly relevant to readers who want to understand the handoffs between data, training, deployment, and operational oversight—not treat each tool as an isolated product.

Follow projects from preparation to production

Practical chapters apply the platform ideas to object-detection and movie-recommendation projects. Readers encounter data preparation, training and validation pipelines, artifact and experiment tracking, model inference, serving, monitoring, and explainability. The project-based progression helps connect individual implementation choices to the larger system they support.

Extend established practices to LLM applications

The closing section carries the platform approach into LLMOps. It develops a retrieval-augmented generation application and addresses observability, prompt management, evaluation and testing, safety guardrails, governance, and cost optimization. Rather than treating LLM work as separate from production engineering, the book examines how familiar operational concerns evolve when systems generate responses.

A useful fit for hands-on ML engineers

Written for data scientists and software engineers, with Python examples, this book will interest readers who want to move beyond notebooks and understand how production ML and AI systems are assembled and operated. Its broad toolset also offers practitioners a practical view of how platform components can support the full model life cycle. 💻