Yesterday we learned how to transform data with Glue and EMR. But in real operations, transformation doesn't happen just once. Every time new data arrives, or at a fixed time each day, we must automatically repeat extract → transform → validate → train → evaluate. If this repetition is done manually, mistakes occur and reproducibility breaks.
Today we cover AWS's two main pillars for automating ML workflows: AWS Step Functions and Amazon SageMaker Pipelines. The MLS-C01 exam frequently asks "which orchestration tool to choose and when."
ML workflows have multiple steps with order, conditions, and dependencies intertwined. For example: