What kind of metadata can I track?

You can track a wide range of metadata including training parameters, dataset references, performance scores, and deployment timestamps.

Can I retrieve performance metrics for specific versions?

Yes, you can ask Claude to fetch and display performance metrics for any specific model version stored in the registry.

Is this skill suitable for MLOps?

Absolutely. It is designed to facilitate automated model versioning and registry management, which are core components of a robust MLOps workflow.

How does this skill track model versions?

The skill interacts with the model-versioning-tracker plugin to log model metadata, version numbers, and performance data directly through Claude's command interface.

Does it integrate with other AI development tools?

Yes, it is built to work in conjunction with other Claude plugins handling data processing, model training, and deployment for a seamless end-to-end experience.

AI Model Versioning Tracker

Name: AI Model Versioning Tracker
Author: jeremylongshore

byjeremylongshore

•

883

Data Science & ML

Automates AI/ML model versioning, lineage tracking, and performance logging within your development environment.

About

This skill empowers Claude to manage complex AI/ML model registries by providing a streamlined interface for tracking model iterations, performance metrics, and deployment history. By utilizing the model-versioning-tracker plugin, it ensures that developers can maintain a clear audit trail of their model development lifecycle, making it easier to reproduce results and monitor model health over time. It is an essential tool for data scientists and MLOps engineers who need to automate model management tasks and integrate version control directly into their AI workflows.

Key Features

Automated model lineage and metadata tracking
Integrated model registry management
Support for versioned model deployment workflows
883 GitHub stars
Performance metrics logging and retrieval
Streamlined interaction with tracking plugins

Use Cases

Retrieving and comparing performance metrics between model versions
Tracking new model iterations with associated training metadata
Implementing automated model registry updates for MLOps pipelines

About

Key Features

Automated model lineage and metadata tracking
Integrated model registry management
Support for versioned model deployment workflows
883 GitHub stars
Performance metrics logging and retrieval
Streamlined interaction with tracking plugins

Use Cases

Retrieving and comparing performance metrics between model versions
Tracking new model iterations with associated training metadata
Implementing automated model registry updates for MLOps pipelines