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.
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
Connect to the MLflow tracking server by setting MLFLOW_TRACKING_URI and verify connectivity with mlflow experiments list.
Create or select an MLflow experiment for the model project using mlflow experiments create --experiment-name <name>.
Log a new model version: start an MLflow run, log parameters (learning rate, epochs, batch size), log metrics (accuracy, loss, F1 score), and log the model artifact with mlflow.<flavor>.log_model().
Register the model in the MLflow Model Registry using mlflow.register_model() with the run URI and a descriptive model name.
Transition the model version through stages: None -> Staging -> Production using client.transition_model_version_stage(). Archive previous production versions.
Compare model versions by querying metrics across runs with mlflow.search_runs() and generating comparison tables showing metric improvements between versions.
Generate a model card from the registered model metadata, including training data description, evaluation metrics, intended use, limitations, and ethical considerations. See ${CLAUDE_SKILL_DIR}/assets/model_card_template.md.
Set up automated alerts for model performance degradation by comparing production metrics against baseline thresholds stored in the model registry.
See ${CLAUDE_SKILL_DIR}/assets/example_mlflow_workflow.yaml for a complete workflow configuration.
Examples
Tracking a new image classification model version: Log a ResNet-50 fine-tuned on a custom dataset. Record hyperparameters (lr=0.001, epochs=50, optimizer=Adam), metrics (val_accuracy=0.94, val_loss=0.18, inference_latency_ms=12), and the serialized model artifact. Register as version 3 in the model registry and transition to Staging for validation.
Comparing model versions before production promotion: Query MLflow for all versions of the sentiment-analysis model. Generate a comparison table showing accuracy improved from 0.87 (v2) to 0.91 (v3) while inference latency increased from 8ms to 15ms. Recommend promoting v3 to Production only if latency is acceptable for the use case.
Generating a model card for compliance review: Extract metadata from MLflow model registry version 5: training dataset (100K customer reviews), evaluation results (F1=0.89 on held-out test set), known limitations (struggles with sarcasm and multilingual input), and intended use (customer feedback classification). Output a structured Markdown model card.
Output
MLflow run with logged parameters, metrics, and model artifact
Model registry entry with version number and stage assignment
Version comparison table with metric deltas across runs
Model card in Markdown format documenting lineage, performance, and limitations
Error Handling
Error
Cause
Solution
MLflow connection refused
Tracking server not running or wrong URI
Verify MLFLOW_TRACKING_URI is correct; start server with mlflow server --host 0.0.0.0 --port 5000
Artifact upload failed
Insufficient permissions on artifact store
Check S3/GCS bucket permissions; verify IAM role has write access to the artifact path
Model registration conflict
Model name already exists with incompatible schema
Use a versioned model name or delete the conflicting registry entry
Metrics not logged
MLflow run ended before logging completed
Ensure all log_metric() calls happen within the active run context (with mlflow.start_run():)
Stage transition denied
Model version already in target stage
Archive the existing version in that stage first, then retry the transition
