Is this skill compatible with other DevOps tools?

Yes, it is designed to integrate with data processing, training, and deployment plugins to create a unified AI/ML DevOps (MLOps) pipeline.

How does this skill help with ML model management?

It automates the process of logging new model versions and retrieving performance data, serving as an intelligent bridge between Claude and your model registry plugin.

Does it support model lineage tracking?

Absolutely; it provides the infrastructure to maintain a clear and searchable history of how your AI models have evolved over time.

Can I track metrics for older model versions?

Yes, the skill allows you to query the versioning tracker to retrieve historical performance data and metrics for any previously logged version.

AI Model Versioning Tracker

Name: AI Model Versioning Tracker
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Manages AI/ML model versions, performance metrics, and lineage tracking directly within the Claude Code environment.

About

This skill streamlines the AI/ML lifecycle by enabling Claude to interact with a dedicated model versioning tracker plugin. It automates the logging of model metadata, version lineage, and performance metrics, ensuring that developers can maintain rigorous version control and audit trails for their machine learning experiments. By facilitating best practices in model registry management, it helps teams optimize their workflows from initial training through to deployment and monitoring.

Key Features

Seamless integration with deployment and monitoring workflows
Automated model version logging and registry entry creation
Standardized implementation of model version control best practices
3 GitHub stars
Comprehensive lineage tracking for AI/ML model development
Real-time retrieval of performance metrics for specific versions

Use Cases

Registering a new image classification model version with associated metadata
Auditing the lineage of an AI model to ensure experiment reproducibility
Comparing performance metrics across different sentiment analysis model iterations

About

Key Features

Seamless integration with deployment and monitoring workflows
Automated model version logging and registry entry creation
Standardized implementation of model version control best practices
3 GitHub stars
Comprehensive lineage tracking for AI/ML model development
Real-time retrieval of performance metrics for specific versions

Use Cases

Registering a new image classification model version with associated metadata
Auditing the lineage of an AI model to ensure experiment reproducibility
Comparing performance metrics across different sentiment analysis model iterations