Introducing mlflow
summary
R API for MLflow demonstrates concrete workflows: use Tracking, Projects, Models to log hyperparameters (example fuel=42), metrics and artifacts; train a small Keras feed‑forward net on Iris, watch loss drop and accuracy rise across epochs, save the model artifact and serve it via REST (served at port 1890) to return class probabilities; run a GitHub Project that launches a Spark random forest on Iris, recording exact commit and run id for reproducibility.
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