Machine Learning Engineering

Andriy Burkov · Curated 2026-09-26

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Machine Learning Engineering Andriy Burkov

Andriy Burkov’s guide to engineering a machine-learning system beyond the trained model.

What it covers

Machine Learning Engineering is Andriy Burkov’s engineering-focused book. It is recommended here for readers moving from model development toward a maintained system; the author-owned site establishes the book and authorship.

Why it matters

A deployment decision includes data handling, serving and operational ownership as well as model quality. This is a reading recommendation, not a measured claim that maintenance always costs more than training or that drift is the leading cause of every failure.

What it made me think

Before optimizing a model, write down how the deployed system’s behavior will be observed and who will respond when it changes. That makes the engineering question concrete.

The short version

Who maintains the system after the model is trained?

My take

Operational advice becomes useful when it names what will be measured and who will act on it. A model upgrade cannot substitute for that plan, but robust engineering is not a guarantee against every modeling error.

How it connects

• Model evaluation alongside system evaluation
• Data quality and monitoring ownership
• Training versus operational budgets

Bottom line

Use the book as an engineering reading guide, then validate its recommendations in the target system.

Takeaways

• Plan for the system that uses the model, not only a training run.
• Measure drift and other failure causes in the intended deployment rather than assuming their ranking.
• Estimate training and operational costs separately.
• Assign ownership for monitoring and recovery.

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Topics: ml-ops, engineering, production, infrastructure