What is Databricks MLflow?

Databricks MLflow is an open source platform designed to simplify and streamline the process of building, deploying, and managing machine learning models. With MLflow, data scientists, developers, and engineers can easily create, track, and compare different experiments in a single environment.

It supports popular machine learning libraries, such as Scikit-learn and TensorFlow, and can be used to develop models for on-premises, cloud-based, or hybrid environments. MLflow also provides an intuitive user interface that makes it easy to visualize and compare the results of experiments, enabling users to rapidly identify the best model for their needs.

Additionally, MLflow's automated model management capabilities make it simple to deploy models in production and monitor their performance over time. Whether you're a data scientist, engineer, or developer, Databricks MLflow is the perfect tool to help you develop, deploy, and manage your machine learning models.

Use Cases And Features

1. Create, track and compare experiments using MLflow's intuitive UI.

2. Easily deploy models in production with automated model management.

3. Monitor model performance over time with real-time analytics.