Solving the World’s Toughest Problems with the Growing Open Source Ecosystem and Databricks
We started Databricks in 2013 in a tiny little office in Berkeley with the belief that data has the potential to solve the world’s toughest problems. We entered 2020 as a global organization with over 1000 employees and a customer base spanning from two-person startups to Fortune 10s. In this blog post, let’s take a...
Automate deployment and testing with Databricks Notebook + MLflow
Today many data science (DS) organizations are accelerating the agile analytics development process using Databricks notebooks. Fully leveraging the distributed computing power of Apache Spark™, these organizations are able to interact easily with data at multi-terabytes scale, from exploration to fast prototype and all the way to productionize sophisticated machine learning (ML) models. As fast...
Better Machine Learning through Active Learning
Try this notebook to reproduce the steps outlined below Machine learning models can seem like magical savants. They can distinguish hot dogs from not-hot-dogs, but that's long since an easy trick. My aunt's parrot can do that too. But machine-learned models power voice-activated assistants that effortlessly understand noisy human speech, and cars that drive themselves...
Processing Geospatial Data at Scale With Databricks
The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is...
New Databricks Integration for Jupyter Bridges Local and Remote Workflows
Introduction For many years now, data scientists have developed specific workflows on premises using local filesystem hierarchies, source code revision systems and CI/CD processes. On the other side, the available data is growing exponentially and new capabilities for data analysis and modeling are needed, for example, easily scalable storage, distributed computing systems or special hardware...
Deep Learning Tutorial Demonstrates How to Simplify Distributed Deep Learning Model Inference Using Delta Lake and Apache Spark™
On October 10th, our team hosted a live webinar—Simple Distributed Deep Learning Model Inference—with Xiangrui Meng, Software Engineer at Databricks. Model inference, unlike model training, is usually embarrassingly parallel and hence simple to distribute. However, in practice, complex data scenarios and compute infrastructure often make this "simple" task hard to do from data source to...
Using AutoML Toolkit’s FamilyRunner Pipeline APIs to Simplify and Automate Loan Default Predictions
Introduction In the post Using AutoML Toolkit to Automate Loan Default Predictions, we had shown how the Databricks Labs’ AutoML Toolkit simplified Machine Learning model feature engineering and model building optimization (MBO). It also had improved the area-under-the-curve (AUC) from 0.6732 (handmade XGBoost model) to 0.723 (AutoML XGBoost model). With AutoML Toolkit’s Release 0.6.1, we...
Scaling Hyperopt to Tune Machine Learning Models in Python
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 popular open-source libraries for tuning Machine Learning models in Python. We’re excited to announce that Hyperopt 0.2.1 supports distributed tuning via Apache Spark. The new SparkTrials class allows you to scale out...
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...
Democratizing Financial Time Series Analysis with Databricks
Introduction The role of data scientists, data engineers, and analysts at financial institutions includes (but is not limited to) protecting hundreds of billions of dollars worth of assets and protecting investors from trillion-dollar impacts, say from a flash crash. One of the biggest technical challenges underlying these problems is scaling time series manipulation. Tick data,...