jcorpac ML Ops Pipelines
ML is not code; it's code + data. Pipelines ensure that your training and deployment are predictable.
The Workflow
- Ingestion: Loading data from sources.
- Validation: Checking data quality (TFX, Great Expectations).
- Transformation: Feature engineering.
- Training: Model creation.
- 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.