Individual skill
tracking-model-versions
Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. it should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
plugins/ai-ml/model-versioning-tracker/skills/tracking-model-versions/SKILL.md
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- Last checked
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- Repository last updated
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- License
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- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- ai Declared skill tag
- performance Declared skill tag
- tracking-model Declared skill tag
- engineering Derived category
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SKILL.mdcore · 5,339 bytesassets/README.mddoc · 362 bytesassets/example_mlflow_workflow.yamlasset · 3,424 bytesassets/model_card_template.mddoc · 6,438 bytesassets/versioning-diagram-brief.mddoc · 730 bytesreferences/README.mddoc · 67 bytesscripts/README.mddoc · 490 bytesscripts/version_control.pyruns · 2,877 bytes
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# Model Versioning Tracker
## Overview
Track and manage AI/ML model versions using MLflow, DVC, or Weights & Biases. Log model metadata (hyperparameters, training data hash, framework version), record evaluation metrics (accuracy, F1, latency), manage model registry transitions (Staging, Production, Archived), and generate model cards documenting lineage and performance.
## Prerequisites
- MLflow tracking server running locally or remotely (`mlflow server` or managed MLflow)
- Python 3.9+ with `mlflow`, `pandas`, and the relevant ML framework installed
- Model artifacts accessible on the local filesystem or cloud storage (S3, GCS)
- Write access to the MLflow tracking URI and artifact store
## Instructions
