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New Databricks Integration for Jupyter Bridges Local and Remote Workflows

December 2, 2019 by Bernhard Walter in
Introduction For many years now, data scientists have developed specific workflows on premises using local filesystem hierarchies, source code revision systems and CI/CD...

Deep Learning Tutorial Demonstrates How to Simplify Distributed Deep Learning Model Inference Using Delta Lake and Apache Spark™

November 20, 2019 by Cyrielle Simeone in
On October 10th, our team hosted a live webinar— Simple Distributed Deep Learning Model Inference —with Xiangrui Meng, Software Engineer at Databricks. Model...

Using AutoML Toolkit's FamilyRunner Pipeline APIs to Simplify and Automate Loan Default Predictions

November 5, 2019 by Jas Bali and Denny Lee in
Try this Loan Risk with AutoML Pipeline API Notebook in Databricks Introduction In the post Using AutoML Toolkit to Automate Loan Default Predictions...

Scaling Hyperopt to Tune Machine Learning Models in Python

October 28, 2019 by Joseph Bradley and Max Pumperla in
Try the Hyperopt notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more. Hyperopt is one of the...

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...

Democratizing Financial Time Series Analysis with Databricks

October 8, 2019 by Ricardo Portilla in
Try this notebook in Databricks Introduction The role of data scientists, data engineers, and analysts at financial institutions includes (but is not limited...

Analyzing Your MLflow Data with DataFrames

October 2, 2019 by Max Allen in
Max Allen interned with Databricks Engineering in the Summer of 2019. This blog post, written by Max, highlights the great work he did...

Using AutoML Toolkit to Automate Loan Default Predictions

September 10, 2019 by Benjamin Wilson, Amy Wang and Denny Lee in
Download the following notebooks and try the AutoML Toolkit today: Evaluating Risk for Loan Approvals using XGBoost (0.90) | Using AutoML Toolkit to...

Guest Blog: How Virgin Hyperloop One Reduced Processing Time from Hours to Minutes with Koalas

August 22, 2019 by Patryk Oleniuk and Sandhya Raghavan in
Watch the on-demand webinar to learn more: From pandas to Koalas: reducing Time-to-Insights for Virgin Hyperloop's Data At Virgin Hyperloop One, we work...

AutoML on Databricks: Augmenting Data Science from Data Prep to Operationalization

August 19, 2019 by Clemens Mewald in
Thousands of data science jobs are going unfilled today as global demand for the talent greatly outstrips supply. Every day, businesses pay the...