MLflow 1.12 Features Extended PyTorch Integration
MLflow 1.12 features include extended PyTorch integration, SHAP model explainability, autologging MLflow entities for supported model flavors, and a number of UI and document improvements. Now available on PyPI and the docs online, you can install this new release with pip install mlflow==1.12.0 as described in the MLflow quickstart guide. In this blog, we briefly...
Managed MLflow Now Available on Databricks Community Edition
In February 2016, we introduced Databricks Community Edition, a free edition for big data developers to learn and get started quickly with Apache Spark. Since then our commitment to foster a community of developers remains steadfast: to date, we have over 150K registered Community Edition users; we have trained thousands of people at meetups and...
Scalable End-to-End Deep Learning using TensorFlow™ and Databricks: On-Demand Webinar and FAQ Now Available!
On July 9th, our team hosted a live webinar—Scalable End-to-End Deep Learning using TensorFlow™ and Databricks—with Brooke Wenig, Data Science Solutions Consultant at Databricks and Sid Murching, Software Engineer at Databricks. In this webinar, we walked you through how to use TensorFlow™ and Horovod (an open-source library from Uber to simplify distributed model training) on...
