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Data & MLOps

The engineering that turns models into reliable products — data pipelines, deployment, and monitoring.

A trained AI model is only useful if it actually works for real people, day after day. Getting it there takes a lot of unglamorous engineering, and that's what Data & MLOps is about.

Think of a restaurant. A brilliant recipe (the model) isn't enough. You need reliable deliveries of fresh ingredients (data), a kitchen that cooks the dish the same way every time (training and serving), and a manager who tastes plates all night to catch anything gone stale (monitoring). MLOps is that whole kitchen operation for AI: gathering and cleaning data, packaging the model so apps can use it, and watching it constantly so quality never quietly slips. Without it, even a genius model becomes an unreliable one-off.

The main ideas

  • Data engineering — Collecting, cleaning, labelling, and versioning the data models learn from.
  • Feature & training infrastructure — Feature stores, experiment tracking, and reproducible training.
  • Deployment & serving — Shipping models as APIs, at the edge, or on-device; latency and cost.
  • Monitoring & evaluation — Detecting drift, regressions, and failures in production; continuous evals.
  • Vector databases — Storing embeddings for semantic search and RAG at scale.

Machine Learning · NLP & Large Language Models · Building with AI

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