Today we are excited to announce the release of MLflow 1.0. Since its launch one year ago, MLflow has been deployed at thousands of organizations to manage their production machine learning workloads, and has become generally available on services like Managed MLflow on Databricks. The MLflow community has grown to over 100 contributors, and the MLflow PyPI package download rate has reached close to 600K times a month. The 1.0 release not only marks the maturity and stability of the APIs, but also adds a number of frequently requested features and improvements.
The release is publicly available starting today. Install MLflow 1.0 using PyPl, read our documentation to get started, and provide feedback on GitHub. Below we describe just a few of the new features in MLflow 1.0. Please refer to the release notes for a full list.
And it looks like they’re going to keep pushing on it from there.