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MLOps

Take models from notebook to reliable production: experiment tracking, reproducibility, data validation, registries, CI gates, serving, shadow and canary releases, drift monitoring and retraining, with MLflow and scikit-learn examples run.

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What you'll learn

  • Explain why ML systems fail in production and what the MLOps lifecycle adds to model development.
  • Track experiments, make training reproducible and package models with metadata using MLflow and standard tools.
  • Validate production data and prevent training-serving skew and point-in-time leakage.
  • Promote models through a registry with automated evaluation gates and CI checks.
  • Deploy with batch or online serving, shadow tests and canary releases, and monitor drift and decay.
  • Plan retraining, rollback and governance for models in production.

Syllabus

Why MLOps Exists

  1. How ML Systems Fail in Production
  2. The MLOps Lifecycle
  3. Maturity Levels and Roles

Experiment Tracking and Reproducibility

  1. Experiment Tracking With MLflow
  2. Reproducible Training
  3. Packaging Models With Metadata

Data in Production

  1. Data Validation
  2. Training-Serving Skew
  3. Feature Stores and Point-in-Time Correctness

Model Registry and Continuous Integration

  1. Model Registry, Versions and Aliases
  2. Testing ML Code, Data and Models
  3. Automated Evaluation Gates

Deploying Models Safely

  1. Batch, Online and Streaming Serving
  2. Serving Performance: Latency and Batching
  3. Shadow Deployment
  4. Canary Releases and A/B Tests

Monitoring Models in Production

  1. What to Monitor
  2. Detecting Data Drift
  3. Delayed Labels and Proxy Metrics

Retraining, Rollback and Incidents

  1. Concept Drift and Model Decay
  2. Retraining Pipelines and Triggers
  3. Rollback and Incident Response

Governance, LLMOps and a Checklist

  1. Governance: Lineage, Model Cards and Access
  2. How LLMOps Differs
  3. An MLOps Readiness Checklist