MLOps Lifecycle Manager

ML lifecycle orchestration automates the transition from experimental notebooks to production by providing executable blueprints for experiment tracking, model versioning, and deployment. It generates deployment manifests and governance artifacts.

Automated retraining triggers are set up by configuring model monitoring and drift detection within the lifecycle workflow. When performance degrades or drift is detected, the system automatically initiates retraining workflows to update the model.

Model versioning and registry management supports multi-environment pipelines across batch, online, and edge deployments. It tracks configurations and versions to ensure consistent releases and governance-compliant artifacts.

MLOps lifecycle management supports governance-compliant releases by generating governance artifacts and tracking experiment configurations. It ensures deployment manifests and dashboards meet compliance requirements before moving to production.

Model monitoring and drift detection are configured during the deployment design phase. The lifecycle orchestration outputs dashboards that visualize model performance and automatically flag drift or degradation in production environments.

You need to provide a SKILL.md frontmatter with a name and description to define the executable blueprint. Optional resources like scripts or references can be included to customize experiment tracking and deployment outputs.