Machine Learning Engineering

Many machine-learning projects run into trouble before the model is the real issue. They may be poorly scoped, disconnected from a business need, difficult to maintain, or never make the journey from prototype to production. In Machine Learning Engineering, Ben Wilson turns attention to the working practices around the model: the planning, communication, experimentation, and engineering that can help a project become useful beyond the notebook.

This Version 2 MEAP edition takes a broad, practical view of the discipline. It is not a tour of algorithms or a deep dive into one ML specialty; its focus is how teams organize and build machine-learning work that can be deployed, maintained, and improved.

Make the path from idea to production clearer

The book begins with the role of machine learning engineering and the reasons projects are abandoned or fall short. Its roadmap moves through planning and scoping, project communication, experimentation, prototype evaluation, and the transition toward an MVP. Later sections turn to resilient development practices, production code, acceptance testing, reworking, model support, and automation.

Engineering practice, not algorithm collecting

Wilson emphasizes the decisions surrounding a model: translating a business problem into a workable project, choosing an appropriate level of complexity, and building code that can be tested and maintained. The value of this perspective is its attention to the connective work between research and deployment—the details that can determine whether a promising experiment becomes a dependable system.

Who may find it useful

The opening guidance is intended for readers with moderate knowledge of applied machine learning and the ability to build an ML solution in Python or Scala. Data scientists and practitioners who want to strengthen the planning, development, and production-readiness of their projects may find its process-focused approach especially relevant.

An early-access version of the work

This is the 2020 Version 2 MEAP manuscript. The completed book was later published by Manning under the title Machine Learning Engineering in Action; this file represents the earlier early-access version rather than that finished edition.