Training Pipeline

Training Pipeline

When to Use

When building the end-to-end system that takes raw data and produces a validated, registered model — reproducibly.

Core Jobs

1. Define Pipeline Stages

Data → Validate → Preprocess → Train → Evaluate → Register

Each stage:

• Takes versioned inputs
• Produces versioned outputs
• Is independently re-runnable (idempotent)
• Has pass/fail criteria

2. Orchestrate the Pipeline

Tools: Airflow, Prefect, Kubeflow Pipelines, Metaflow, SageMaker Pipelines

• Each step = one container or task
• Pass artifacts between steps via shared storage (S3, GCS)
• Log metadata to experiment tracker at each step

3. Version Everything

• Data: DVC or dataset versioning in the registry
• Code: git commit hash logged with each run
• Environment: Docker image tag (pinned, not latest)
• Model: versioned in model registry with lineage to data + code

4. Automated Evaluation Gate

Before registering, automatically check:

• Performance > baseline (current prod model)
• Performance on recent data slice (last 30 days)
• No data leakage detected (temporal split verified)
• Latency within SLA If any check fails: pipeline fails, Slack alert, no model registered.

5. CI/CD for ML

• PR → run unit tests on pipeline code
• Merge → trigger training run on subset (smoke test)
• Tag/release → full training run → deploy if gates pass

Key Outputs

• Pipeline DAG definition
• Evaluation gate configuration
• Model registry entry (with lineage)
• Pipeline monitoring dashboard

Anti-Patterns

• Manual steps in the pipeline (not reproducible)
• No evaluation gate — any model can go to prod
• latest tags on Docker images or data
• No lineage — can't trace a prod model back to its data