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MLOps · Module 1 of 10

What is MLOps?

MLOps applies DevOps practices to machine learning so models work reliably in production.

📖 Key ideas

  1. Most ML projects fail to reach production without good processes.
  2. MLOps covers data, training, deployment, monitoring and governance.
  3. It unites data scientists, ML engineers and operations.
  4. Goals: reproducibility, automation, reliability, faster iteration.
  5. Tools: MLflow, DVC, Kubeflow, SageMaker, Vertex AI.
💡 Real-life example: MLOps is the factory process that turns a lab prototype into a product shipped every day.
MLOpsMachine Learning Operations
ML lifecycleData → train → deploy → monitor
ReproducibilitySame inputs give same outputs
ML engineerBuilds production ML systems