MLflow
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, covering experiment tracking, reproducible runs, model packaging, and deployment.
4 Steps
- 1
Install MLflow: Install MLflow using pip. This will provide the core MLflow functionalities.
- 2
Track an Experiment: Track a simple experiment by logging parameters and metrics. This example simulates training a model and logging the training parameters and the resulting accuracy.
- 3
Run the MLflow UI: Start the MLflow UI to view the tracked experiment. This command will launch a local server where you can explore your experiments.
- 4
Log a Model: Log a trained model to MLflow. This example uses scikit-learn to train a simple linear regression model and then logs it to MLflow.
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