ML Ops Pipelines

ML is not code; it's code + data. Pipelines ensure that your training and deployment are predictable.

The Workflow

1. Ingestion: Loading data from sources.
2. Validation: Checking data quality (TFX, Great Expectations).
3. Transformation: Feature engineering.
4. Training: Model creation.
5. Deployment: Pushing to the model registry or serving layer.

Tools

• ZenML: A framework-agnostic MLOps pipeline tool.
• Kubeflow: Running ML workflows on Kubernetes.
• GitHub Actions: For simple, CI-based ML pipelines.

Best Practices

• Versioning: Version your data AND your code.
• Automated Retraining: Trigger pipelines when data drift is detected.