Last week focused on "how to deploy a model"; this week pivots to "how to automate and make that deployment reproducible". That is MLOps. The sequence data preprocessing → training → evaluation → registration → deployment, executed by hand running notebook cells every time, lacks reproducibility and invites errors. SageMaker Pipelines bundles this workflow as code-defined DAGs, so once defined, the same procedure repeats via a button click or trigger, automatically.
Today we cover Pipelines' core building blocks: Steps, Directed Acyclic Graph (DAG), Parameters, and Condition Steps