[Verse 1] From raw data lakes to deployment dreams Build your pipeline like assembly lines Orchestrate each step, no missing seams Airflow schedules tasks at proper times Kubeflow manages your container fleet While Prefect flows through every feat [Chorus] Pipeline flows from source to store Feature gates and validation doors Automate, orchestrate, validate more ML pipelines are your core Version data, test your models CI-CD solves deployment throttles [Verse 2] Feast serves features fresh and fast Tecton builds your feature store Hopsworks makes your datasets last No more manual feature chore Delta Lake versions every change DVC keeps your data range [Chorus] Pipeline flows from source to store Feature gates and validation doors Automate, orchestrate, validate more ML pipelines are your core Version data, test your models CI-CD solves deployment throttles [Bridge] MLflow registry holds your trained minds SageMaker endpoints serve predictions Terraform builds infrastructure designs Dagster orchestrates your convictions Testing gates before you ship Never let bad models slip [Verse 3] Dependencies mapped in DAG formations Each node waits for upstream completion Automated testing runs validations Model metrics check for depletion Deployment triggers fire when ready Keep your production pipeline steady [Chorus] Pipeline flows from source to store Feature gates and validation doors Automate, orchestrate, validate more ML pipelines are your core Version data, test your models CI-CD solves deployment throttles [Outro] From notebooks to production scale Your pipeline tells the automation tale Build once, deploy without fail ML orchestration will never pale
← Unit 5.1 — Model Deployment & Serving | Unit 5.3 — Monitoring, Observability & Drift Detection →