• 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.
• hyperparam shows how to do hyperparameter tuning with MLflow and some popular optimization libraries.
• keras modifies a Keras classification example and uses MLflow's mlflow.tensorflow.autolog() API to automatically log metrics and parameters to MLflow during training.
• 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.
• r_wine demonstrates how to log parameters, metrics, and models from R.
• 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 uses MLproject to set up a Conda environment, define parameter types and defaults, entry point for training, etc.
• sklearn_logistic_regression is a simple MLflow example with hooks to log training data to MLflow tracking server.
• supply_chain_security shows how to strengthen the security of ML projects against supply-chain attacks by enforcing hash checks on Python packages.
• tensorflow contains end-to-end one run examples from train to predict for TensorFlow 2.8+ It includes usage of MLflow's mlflow.tensorflow.autolog() API, which captures TensorBoard data and logs to MLflow with no code change.
• docker demonstrates how to create and run an MLflow project using docker (rather than conda) to manage project dependencies
• fastai modifies a fastai classification example and highlights MLflow's mlflow.fastai.autolog() API to track parameters, metrics, and artifacts while training a simple MNIST model.
• johnsnowlabs gives you access to 20.000+ state-of-the-art enterprise NLP models in 200+ languages for medical, finance, legal and many more domains.