MLflow Model Registry

One collaborative hub for all machine learning models

MLflow Model Registry is a collaborative hub where teams can share ML models, work together from experimentation to online testing and production, integrate with approval and governance workflows, and monitor ML deployments and their performance.

Avantages

ONE COLLABORATIVE HUB

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.

AUTOMATE MODEL LIFECYCLE MANAGEMENT

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.

VISIBILITY AND GOVERNANCE

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.

Fonctionnalités

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.

Stades de modèles : Étapes prédéfinies ou personnalisées assignées à chaque version de modèle, telle que « pré-production » ou « production » pour représenter le cycle de vie d'un modèle.

Transitions entre stades de modèles : Conservez les nouveaux événements d'enregistrement ou leurs modifications sous forme d'activités gardant automatiquement une trace des utilisateurs, changements et nouvelles métadonnées telles que les commentaires.

Intégrations de workflows CI/CD : Enregistrez les transitions entre les stades, demandez, évaluez et approuvez des changements dans le cadre de pipelines CI/CD pour un contrôle et une gouvernance optimisés.

Model Serving: Quickly serve machine learning models as RESTful APIs for online testing, dashboard updates, etc. on Databricks

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