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MLflow Model Registry Integration

Overview

IdeaWeaver provides seamless integration with MLflow for experiment tracking and model registration. This guide shows you how to use MLflow with IdeaWeaver for model management.

Setup

1. Start the MLflow server

source ideaweaver-env/bin/activate
mlflow server --host 127.0.0.1 --port 5000 --backend-store-uri sqlite:///mlflow.db

2. Train and register a model

ideaweaver train \
  --model bert-base-uncased \
  --dataset ./datasets/training_data.csv \
  --track-experiments \
  --mlflow-uri http://127.0.0.1:5000 \
  --mlflow-experiment "MyExperiment" \
  --register-model

Example Output

🤗 Using model: bert-base-uncased
📊 Experiment tracking enabled
🏷️  Model registration enabled
🚀 Starting model training...
2025/06/05 11:08:39 INFO mlflow.tracking.fluent: Experiment with name 'MyExperiment' does not exist. Creating a new experiment.
...

MLflow UI Example

MLflow UI Example

You can view your run and registered model in the MLflow UI at http://127.0.0.1:5000.

Features

  • Experiment tracking
  • Model versioning
  • Model registration
  • Performance metrics logging
  • Artifact storage
  • Model deployment

Best Practices

  1. Experiment Organization
  2. Use meaningful experiment names
  3. Tag experiments appropriately

  4. Document experiment parameters

  5. Model Registration

  6. Version your models
  7. Add model descriptions

  8. Track model lineage

  9. Performance Tracking

  10. Log all relevant metrics
  11. Track resource usage
  12. Monitor model performance

Troubleshooting

Common Issues

  1. Connection Errors
  2. Verify MLflow server is running
  3. Check network connectivity

  4. Validate URI format

  5. Authentication Issues

  6. Check credentials
  7. Verify permissions
  8. Ensure proper access

Debug Mode

Enable verbose output for debugging:

ideaweaver train \
  --model bert-base-uncased \
  --dataset ./datasets/training_data.csv \
  --track-experiments \
  --mlflow-uri http://127.0.0.1:5000 \
  --verbose

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