Can I use this with Nixtla and TimeGPT?

Yes, this skill is specifically designed to work with Nixtla plugin concepts, making it ideal for managing and benchmarking time-series forecasting models.

How does this skill help with ML Ops?

It automates the logging of model versions and performance metrics, creating a reliable audit trail for your machine learning lifecycle within your development environment.

How do I track a new model version?

Simply ask Claude to track your model, and the skill will generate the necessary code to interface with the model-versioning-tracker plugin to log your version and metadata.

Does it support performance monitoring?

Absolutely. You can retrieve and compare metrics like accuracy, loss, or custom performance scores across different versions of your stored models.

Model Versioning Tracker

Name: Model Versioning Tracker
Author: intent-solutions-io

byintent-solutions-io

Ciencia de Datos y ML

Tracks and manages AI/ML model versions, performance metrics, and lineage within Claude Code workflows.

Acerca de

The Model Versioning Tracker skill enables Claude to interface directly with model registries and versioning plugins, allowing developers to automate the logging of AI/ML model iterations. It facilitates performance monitoring, lineage tracking, and historical comparison, ensuring that machine learning deployments remain organized, reproducible, and well-documented. This skill is essential for data scientists and ML engineers using frameworks like Nixtla or TimeGPT who need to maintain a rigorous audit trail of their model evolution while optimizing for performance.

Características Principales

Detailed lineage tracking for AI/ML model iterations
Seamless integration with Nixtla and TimeGPT pipelines
Automated metadata management for model deployment
0 GitHub stars
Automated model version logging and registration
Performance metric retrieval and historical comparison

Casos de Uso

Retrieving and comparing performance metrics across multiple sentiment analysis iterations
Implementing an automated model registry workflow for time-series forecasting pipelines
Registering and tracking a new version of an image classification model

Acerca de

Características Principales

Detailed lineage tracking for AI/ML model iterations
Seamless integration with Nixtla and TimeGPT pipelines
Automated metadata management for model deployment
0 GitHub stars
Automated model version logging and registration
Performance metric retrieval and historical comparison

Casos de Uso

Retrieving and comparing performance metrics across multiple sentiment analysis iterations
Implementing an automated model registry workflow for time-series forecasting pipelines
Registering and tracking a new version of an image classification model