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Data & MLOps

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

Data & MLOps is one of the core areas in the AI University map of AI. Explore the diagram, then dive into each topic — every subtopic grows into its own deep-dive over time.

flowchart LR
  DATA[(Data)] --> TRAIN[Train] --> EVAL[Evaluate] --> DEP[[Deploy]] --> MON[Monitor]
  MON -. feedback .-> DATA

Key topics

  • 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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