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Register ML Model

Skill Verified Active

Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

Purpose

To streamline and govern the promotion of trained ML models from experimentation to production environments using MLflow Model Registry.

Features

  • Register models with version control
  • Manage stage transitions (Staging, Production, Archived)
  • Track model lineage with metadata
  • Automate registry operations via CI/CD
  • Implement model aliasing for stable references

Use Cases

  • Promoting a trained model from experimentation to production
  • Managing multiple model versions across development stages
  • Implementing model approval workflows for governance
  • Tracking model lineage from training to deployment

Non-Goals

  • Direct model training or hyperparameter optimization
  • Deployment of models to serving infrastructure
  • Automated A/B testing infrastructure setup beyond alias configuration

Workflow

  1. Configure MLflow Model Registry Backend
  2. Register Model from Training Run
  3. Implement Stage Transitions with Validation
  4. Implement Model Aliasing and References
  5. Implement Model Lineage Tracking
  6. Automate Registry Operations with CI/CD

Practices

  • Model versioning
  • MLOps workflow
  • CI/CD automation
  • Governance
  • Auditing

Prerequisites

  • MLflow tracking server with Model Registry enabled
  • Trained model logged with MLflow
  • Python environment with MLflow installed

Protocol

  • info:Idempotent retry & timeoutsThe MLflow client library likely handles retries and timeouts for its API calls. The provided code does not explicitly implement custom retry logic or hard timeouts for MLflow operations.

Installation

/plugin install agent-almanac@pjt222-agent-almanac

Quality Score

Verified
97 /100
Analyzed about 16 hours ago

Trust Signals

Last commit1 day ago
Stars14
LicenseMIT
Status
View Source

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