Fast Parallel Testing at Databricks with Bazel
The Databricks Developer Tools team recently completed a project to greatly speed up the pull-request (PR) validation workflows for many of our engineers...
Announcing the MLflow 1.1 Release
We’re excited to announce today the release of MLflow 1.1. In this release, we’ve focused on fleshing out the tracking component of MLflow...
Automated Hyperparameter Tuning, Scaling and Tracking: On-Demand Webinar and FAQs now available!
Try this notebook in Databricks On June 20th, our team hosted a live webinar— Automated Hyperparameter Tuning, Scaling and Tracking on Databricks —with...
Announcing Databricks Runtime 5.5 and Runtime 5.5 for Machine Learning
Databricks is pleased to announce the release of Databricks Runtime 5.5. This release includes Apache Spark 2.4.3 along with several important improvements and...
What's new with MLflow? On-Demand Webinar and FAQs now available!
On June 6th, our team hosted a live webinar— Managing the Complete Machine Learning Lifecycle: What's new with MLflow —with Clemens Mewald, Director...
Scaling Genomic Workflows with Spark SQL BGEN and VCF Readers
Read Rise of the Data Lakehouse to explore why lakehouses are the data architecture of the future with the father of the data...
Accurately Building Genomic Cohorts at Scale with Delta Lake and Spark SQL
Get an early preview of O'Reilly's new ebook for the step-by-step guidance you need to start using Delta Lake. This is the second...
Simplifying Streaming Stock Analysis using Delta Lake and Apache Spark: On-Demand Webinar and FAQ Now Available!
Get an early preview of O'Reilly's new ebook for the step-by-step guidance you need to start using Delta Lake. On June 13th, we...
Detecting Data Bias Using SHAP and Machine Learning
Try the Detecting Data Bias Using SHAP notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more. StackOverflow's...
Hyperparameter Tuning with MLflow, Apache Spark MLlib and Hyperopt
Hyperparameter tuning is a common technique to optimize machine learning models based on hyperparameters, or configurations that are not learned during model training...