Facilitate the sharing of expertise and knowledge about building and deploying machine learning models by making models more discoverable, and providing collaborative features to jointly improve on common ML tasks.
Use webhooks to automate and integrate your machine learning pipeline with existing CI/CD tools and workflows. For example, you can trigger CI builds when a new model version is created or notify your team members through Slack each time a model transition to production is requested.
Large enterprises often have thousands of ML models in the experimentation, testing, and production phases at any point in time. The MLflow Model Registry provides full visibility and enables governance of each by keeping track of model history and managing who can approve changes.
Central Repository: Register MLflow models with the MLflow Model Registry. A registered model has a unique name, version, stage, and other metadata.
Model Versioning: Automatically keep track of versions for registered models when updated.
Model Stage: Assigned preset or custom stages to each model version, like “Staging” and “Production” to represent the lifecycle of a model.
Model Stage Transitions: Record new registration events or changes as activities that automatically log users, changes, and additional metadata such as comments.
CI/CD Workflow Integration: Record stage transitions, request, review and approve changes as part of CI/CD pipelines for better control and governance.
Model Serving: Quickly serve machine learning models as RESTful APIs for online testing, dashboard updates, etc. on Databricks