Tracking Model Versions
About
This skill enables Claude to manage AI/ML model versions using the model-versioning-tracker plugin. Use it when developers need to track model lineage, log performance, implement version control, or work with tools like a model registry or MLflow. It facilitates best practices for model versioning and automates model workflows.
Quick Install
Claude Code
Recommended/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/Tracking Model VersionsCopy and paste this command in Claude Code to install this skill
Documentation
Overview
This skill empowers Claude to interact with the model-versioning-tracker plugin, providing a streamlined approach to managing and tracking AI/ML model versions. It ensures that model development and deployment are conducted with proper version control, logging, and performance monitoring.
How It Works
- Analyze Request: Claude analyzes the user's request to determine the specific model versioning task.
- Generate Code: Claude generates the necessary code to interact with the model-versioning-tracker plugin.
- Execute Task: The plugin executes the code, performing the requested model versioning operation, such as tracking a new version or retrieving performance metrics.
When to Use This Skill
This skill activates when you need to:
- Track new versions of AI/ML models.
- Retrieve performance metrics for specific model versions.
- Implement automated workflows for model versioning.
Examples
Example 1: Tracking a New Model Version
User request: "Track a new version of my image classification model."
The skill will:
- Generate code to log the new model version and its associated metadata using the model-versioning-tracker plugin.
- Execute the code, creating a new entry in the model registry.
Example 2: Retrieving Performance Metrics
User request: "Get the performance metrics for version 3 of my sentiment analysis model."
The skill will:
- Generate code to query the model-versioning-tracker plugin for the performance metrics associated with the specified model version.
- Execute the code and return the metrics to the user.
Best Practices
- Data Validation: Ensure input data is validated before logging model versions.
- Error Handling: Implement robust error handling to manage unexpected issues during version tracking.
- Performance Monitoring: Continuously monitor model performance to identify opportunities for optimization.
Integration
This skill integrates with other Claude Code plugins by providing a centralized location for managing AI/ML model versions. It can be used in conjunction with plugins that handle data processing, model training, and deployment to ensure a seamless AI/ML workflow.
GitHub Repository
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