deployment-automation

Automate GenAI agent deployment by integrating MLflow job triggers that initiate CI/CD steps automatically when new model versions are registered. This streamlines model releases and ensures reliable promotions to production environments.

An evaluation-then-promote workflow is a quality gate where models are evaluated against defined performance thresholds before being promoted. If the model meets benchmarks, it is automatically promoted to the production alias.

Track evaluation dataset lineage in MLflow by linking datasets to runs using mlflow.log_input(). This ensures full traceability between the evaluation data used and the model version being deployed.

Yes, you can organize MLflow experiments to maintain a clear separation of concerns. Dedicated experiments can be set up specifically for agent development, evaluation, and deployment tracking.

Yes, MLflow model registry supports automated model version promotion. When a new version is registered, automated triggers can evaluate it against benchmarks and promote it to production if successful.

The best way to set up CI/CD for GenAI agents is automating deployment triggers, linking evaluation datasets for traceability, and enforcing evaluation-then-promote workflows to ensure reliable model releases.