MLFlow
Empowering machine learning developers with open-source tools for tracking experiments, deploying models, and managing production ML through the MLFlow platform.
MLFlow is an open-source machine learning platform that provides tools for tracking experiments, packaging and deploying models, and managing production ML models, with offerings including a tracking server, APIs, and libraries for popular ML frameworks. MLFlow, available on various platforms and following the Apache 2 license, ensures compatibility across different versions and maintains examples for various use cases.
About MLFlow
MLFlow is an open-source platform for the complete machine learning lifecycle. They develop and provide tools for tracking experiments, packaging and deploying models, and managing production machine learning models. MLFlow's offerings include a tracking server, a tracking API, and a set of libraries for scikit-learn, PyTorch, TensorFlow, Keras, and XGBoost. Their project is available on several platforms such as PyPI, Conda, Maven Central, and CRAN. MLFlow's community has a presence on Slack for discussions and collaboration. They have a wide range of downloads, and their software follows the Apache 2 license. MLFlow also focuses on ensuring compatibility across different versions and maintaining examples for various use cases. Their Maven artifacts support various functionalities like scoring, Spark integration, and cross-version tests.