Three Principles for Selecting Machine Learning Platforms
This blog post is the second in a series on ML platforms, operations, and governance. For the first post, see Rafi Kurlansik’s post…
This blog post is the second in a series on ML platforms, operations, and governance. For the first post, see Rafi Kurlansik’s post…
Try the Hyperopt notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more. Hyperopt is one of the most…
On June 20th, our team hosted a live webinar—Automated Hyperparameter Tuning, Scaling and Tracking on Databricks—with Joseph Bradley, Software Engineer, and Yifan Cao,…
Hyperparameter tuning is a common technique to optimize machine learning models based on hyperparameters, or configurations that are not learned during model training.…
We are excited to announce the release of Databricks Runtime 5.4 ML (Azure | AWS). This release includes two Public Preview features to…
We are excited to announce the release of Databricks Runtime 5.2 for Machine Learning. This release includes several new features and performance improvements…
Today, we are excited to introduce HorovodRunner in our Databricks Runtime 5.0 ML! HorovodRunner provides a simple way to scale up your deep…
Thanks to our awesome interns! This summer, our Engineering interns at Databricks did amazing work. Our interns, working on teams from Developer Tools…
Developing custom Machine Learning (ML) algorithms in PySpark—the Python API for Apache Spark—can be challenging and laborious. In this blog post, we describe…
This is a cross blog post effort between Databricks and Uber Engineering. Yun Ni is a software engineer on Uber’s Machine Learning Platform…