examples/README.md
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 dependenciesjohnsnowlabs gives you access to 20.000+ state-of-the-art enterprise NLP models in 200+ languages for medical, finance, legal and many more domains.demos folder contains notebooks used during MLflow presentations.