“Everyone who has tried to do machine learning development knows that it is complex. The ability to manage, version and share models is critical to minimizing confusion as the number of models in experimentation, testing and production phases at any given time can span into the thousands,” said Matei Zaharia, co-founder and CTO at Databricks. “The new additions in MLflow, developed collaboratively with hundreds of contributors, are enabling organizations worldwide to improve ML development and deployment. With hundreds of thousands of monthly downloads, we are encouraged that the community's contributions are making a positive impact.”
Databricks’ MLflow offering already has the ability to log metrics, parameters, and artifacts as part of experiments, package models and reproducible ML projects, and provide flexible deployment options within the platform or any cloud inference services or containers. The MLflow Model Registry builds on these capabilities by allowing organizations to collaborate on models and optimize the development lifecycle of ML models as they move from being logged into actual deployment through: