Unit 5.2 โ€” ML Pipelines & Orchestration

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[Verse 1]
From raw data to production deployment
We need a pipeline that flows without delay
Airflow orchestrates each transformation
While Kubeflow keeps the models on their way
Prefect and Dagster handle dependencies
Making sure each step runs when it should
Building end-to-end with consistency
From ingestion to serving, understood

[Chorus]
Pipeline, pipeline, data flows through
Orchestrate, validate, deploy what's true
Feature stores and CI-CD too
Pipeline, pipeline, seeing it through
Gates and checks at every stage
Model registry turns the page
Pipeline, pipeline, ML's new age

[Verse 2]
Feast and Tecton serve our features clean
Hopsworks manages what the models need
Consistent data, batch and real-time stream
Feature stores plant the serving seed
Version control with DVC in place
LakeFS tracks every data change we make
Delta Lake maintains the storage space
While pipelines run for business sake

[Chorus]
Pipeline, pipeline, data flows through
Orchestrate, validate, deploy what's true
Feature stores and CI-CD too
Pipeline, pipeline, seeing it through
Gates and checks at every stage
Model registry turns the page
Pipeline, pipeline, ML's new age

[Bridge]
Automated testing guards the gate
Model validation sets the fate
If performance metrics pass the test
Then deployment triggers do the rest
MLflow registry holds each version
SageMaker, Vertex AI conversion
Terraform builds the infrastructure
Pulumi codes the ML architecture

[Verse 3]
CI-CD pipelines run the show
Testing models before they go
Validation gates won't let them pass
Until they meet our quality class
From training jobs to serving layers
Each component has its players
End-to-end automation flows
That's how production ML goes

[Chorus]
Pipeline, pipeline, data flows through
Orchestrate, validate, deploy what's true
Feature stores and CI-CD too
Pipeline, pipeline, seeing it through
Gates and checks at every stage
Model registry turns the page
Pipeline, pipeline, ML's new age

[Outro]
Build it once and let it run
Automated ML has begun
Pipeline, pipeline, job well done

โ† Unit 5.1 โ€” Model Deployment & Serving | Unit 5.3 โ€” Monitoring, Observability & Drift Detection โ†’