quickstart/mlflow_tracking.py
is a basic example to introduce MLflow concepts.
Various examples that depict MLflow tracking, project, and serving use cases.
h2o
depicts how MLflow can be use to track various random forest architectures to train models for predicting wine quality.multistep_workflow
is an end-to-end of a data ETL and ML training pipeline built as an MLflow project. The example shows how parts of the workflow can leverage from previously run steps.pytorch
uses CNN on MNIST dataset for character recognition. The example logs TensorBoard events and stores (logs) them as MLflow artifacts.remote_store
: has a usage example of REST based backed store for tracking.sklearn_elasticnet_diabetes
: Uses the sklearn Diabetes dataset to predict diabetes progression using ElasticNet.sklearn_elasticnet_wine_quality
is an example for MLflow projects. This uses the Wine Quality dataset and Elastic Net to predict quality. The example usesMLproject
to set up a Conda environment, define parameter types and defaults, entry point for training, etc.sklearn_logisic_regression
is a simple MLflow example with hooks to log training data to MLflow tracking server.tensorflow
is an end-to-end one run example from train to predict.