CML Experiments have been rebuilt, leveraging the MLflow ecosystem to complement CML’s existing strengths in model development and deployment. CML now ships the mlflow SDK and an integrated visual experience that enables experiment tracking and comparison via flexible visuals.
Model development is an iterative process that involves many “experiments” to determine the right combination of data, algorithm, and hyperparameters to maximize accuracy. Multiple versions of the “model” are created as the iterative process continues after the initial deployment. To efficiently develop models while addressing the technical and governance needs for traceability and repeatability, data scientists need to be able to track these different experiments and model versions and see which experiments produced which models.
CML Experiment Tracking through MLflow API CML’s experiment tracking features allow you to use the MLflow client library for logging parameters, code versions, metrics, and output files when running your machine learning code. The MLflow library is available in CML Sessions without you having to install it. CML also provides a native UI for later visualizing the experiment results.
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