Company Blog › Machine Learning

In less than a year, MLflow has reached almost 500K monthly downloads, and gathered over 80 code contributors and 40 contributing organizations, confirming the need for an open source approach to help standardize the machine learning lifecycle across tools, teams, and processes. We are thrilled to host some of our key contributors and customers next...

On March 7th, our team hosted a live webinar—Managing the Complete Machine Learning Lifecycle—with Andy Konwinski, Co-Founder and VP of Product at Databricks. In this webinar, we walked you through how MLflow, an open source framework for the complete Machine Learning lifecycle, helps solve for challenges around experiment tracking, reproducible projects and model deployment. Specifically,...

We are excited to announce the general availability (GA) of Databricks Runtime for Machine Learning, as part of the release of Databricks Runtime 5.3 ML. Built on top of Databricks Runtime, Databricks Runtime ML is the optimized runtime for developing ML/DL applications in Databricks. It offers native integration with popular ML/DL frameworks, such as scikit-learn,...

Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models. To address these challenges, last June we introduced MLflow, an open source platform to manage the ML lifecycle that works with any machine learning library and environment. The project has grown...

On January 15th, we hosted a live webinar—Accelerating Machine Learning on Databricks—with Adam Conway, VP of Product Management, Machine Learning, at Databricks and Hossein Falaki, Software Development Engineer and Data Scientist at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular,...