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Data + MLOps

Data and MLOps — From Datasets to Reliable AI Systems

Learn data pipelines, feature stores, model deployment, monitoring, observability, governance and operational AI reliability.

Data is the foundation

Most AI failures are not model failures first. They are data, process, or deployment failures. Reliable AI needs clear data ownership, quality checks, lineage, privacy, and access control.

AI data pipeline

  1. Collect data from systems, documents, sensors, APIs, or user interactions.
  2. Clean, validate, deduplicate, and normalize it.
  3. Create features, embeddings, or labels.
  4. Train, evaluate, and version models.
  5. Deploy models behind APIs or workflows.
  6. Monitor cost, latency, drift, quality, and errors.

What is MLOps?

MLOps applies software engineering discipline to machine learning. It includes versioning data and models, reproducible experiments, CI/CD, monitoring, automated tests, rollback plans, security, and governance.

Monitoring signals

  • Input data quality.
  • Model performance.
  • Latency and uptime.
  • Cost per task or per user.
  • Human override rate.
  • Errors, hallucinations, or policy violations.

Governance

AI systems should define who owns the model, who approves changes, what data is allowed, what risks exist, and how incidents are handled.