How does it integrate with SpecWeave increments?

Models are automatically registered during the increment completion workflow, mapping specific code changes and experiments directly to the resulting model versions for complete traceability.

How does this skill help with model reproducibility?

It tracks every model artifact alongside its specific training metadata, feature schema, and dataset version, ensuring any past model can be fully audited or recreated.

Can I use external registries like MLflow?

Yes, the skill includes built-in providers for MLflow as well as custom storage backends like S3, GCS, or Azure Blob Storage.

What happens if a production model starts underperforming?

You can use the rollback command to manually revert to a previous stable version, or set up auto-rollback triggers that activate if error rates or latency exceed your thresholds.

ML Model Registry

Name: ML Model Registry
Author: anton-abyzov

byanton-abyzov

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データサイエンスとML

Manages the complete machine learning model lifecycle through centralized versioning, staging pipelines, and automated metadata tracking within the SpecWeave framework.

The ML Model Registry skill provides a standardized system for tracking and managing machine learning models from development through production. It addresses critical MLOps challenges such as model reproducibility, fragmented metadata, and risky deployments by offering a single source of truth for version history and model lineage. By integrating directly with SpecWeave increments, it enables developers to seamlessly promote models between staging environments, perform safe rollbacks based on automated performance triggers, and maintain comprehensive audit trails for regulatory compliance.

主な機能

01Automated staging pipeline management from development to production

02Comprehensive model comparison tools for performance and latency metrics

03One-command rollbacks with configurable performance and error triggers

04Centralized versioning with semantic versioning (Major/Minor/Patch) support

0513 GitHub stars

06Detailed metadata tracking including hyperparameters, metrics, and lineage

ユースケース

01Promoting a validated model from staging to production with a clear audit trail

02Implementing automated safety triggers to rollback models that exceed latency thresholds

03Investigating model lineage to identify the exact dataset and code version used for training

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anton-abyzov/specweave model-registry

For use in Claude.ai and ChatGPT

Download Skill