How (Not) To Scale Deep Learning in 6 Easy Steps
Try this notebook in Databricks Introduction: The Problem Deep learning sometimes seems like sorcery. Its state-of-the-art applications are at times delightful and at...
Productionizing Machine Learning with Delta Lake
Get an early preview of O'Reilly's new ebook for the step-by-step guidance you need to start using Delta Lake. Try out this notebook...
Deep Learning on Medical Images at Population Scale: On-Demand Webinar and FAQ Now Available!
On June 26th, we hosted a live webinar — Deep Learning on Medical Images at Population-scale — with members of the data science...
Announcing Databricks Runtime 5.5 with Conda (Beta)
Databricks is pleased to announce the release of Databricks Runtime 5.5 with Conda (Beta). We introduced Databricks Runtime 5.4 with Conda (Beta), with...
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...
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...