A Guide to MLflow Talks at Spark + AI Summit 2019
In less than a year, MLflow has reached almost 500K monthly downloads, and gathered over 80 code contributors and 40 contributing organizations, confirming the need for an open source approach to help standardize the machine learning lifecycle across tools, teams, and processes. We are thrilled to host some of our key contributors and customers next...
A Guide to Healthcare and Life Sciences Talks at Spark + AI Summit 2019
Data and AI are ushering in a new era of precision medicine. The scale of the cloud, combined with advancements in machine learning, are enabling healthcare and life sciences organizations to use their mountains of data—such as electronic health records, genomics, real-world evidence, claims, and more—to drive innovation across the entire ecosystem, from accelerating drug...
Managing the Complete Machine Learning Lifecycle: On-Demand Webinar now available!
On March 7th, our team hosted a live webinar—Managing the Complete Machine Learning Lifecycle—with Andy Konwinski, Co-Founder and VP of Product at Databricks. In this webinar, we walked you through how MLflow, an open source framework for the complete Machine Learning lifecycle, helps solve for challenges around experiment tracking, reproducible projects and model deployment. Specifically,...
Databricks Runtime 5.3 ML Now Generally Available
We are excited to announce the general availability (GA) of Databricks Runtime for Machine Learning, as part of the release of Databricks Runtime 5.3 ML. Built on top of Databricks Runtime, Databricks Runtime ML is the optimized runtime for developing ML/DL applications in Databricks. It offers native integration with popular ML/DL frameworks, such as scikit-learn,...
Introducing Brickchain: Planet-scale Unified Analytics
Today we are excited to announce Brickchain, the next generation technology for zettabyte-scale analytics, by harnessing all the compute power on the planet. Brickchain is the most scalable, secure, and collaborative data technology ever invented. As you may know, Databricks was founded by the original creators of Apache Spark, a unified analytics engine that uses...
Improving Data Management and Analytics in the Federal Government
From Static Data Warehouse to Scalable Insights and AI On-Demand Government agencies today are dealing with a wider variety of data at a much larger scale. From satellite imagery to sensor data to citizen records, petabytes of semi- and unstructured data is collected each day. Unfortunately, traditional data warehouses are failing to provide government agencies...
MLflow v0.9.0 Features SQL Backend, Projects in Docker, and Customization in Python Models
MLflow v0.9.0 was released today. It introduces a set of new features and community contributions, including SQL store for tracking server, support for MLflow projects in Docker containers, and simple customization in Python models. Additionally, this release adds a plugin scheme to customize MLflow backend store for tracking and artifacts. Now available on PyPi and...
Simple Steps to Distributed Deep Learning: On-Demand Webinar and FAQ Now Available!
On February 12th, we hosted a live webinar—Simple Steps to Distributed Deep Learning on Databricks—with Yifan Cao, Senior Product Manager, Machine Learning and Bago Amirbekian, Machine Learning Software engineer at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning, specifically distributed deep learning....
Simplifying Genomics Pipelines at Scale with Databricks Delta
This blog is the first blog in our “Genomics Analysis at Scale” series. In this series, we will demonstrate how the Databricks Unified Analytics Platform for Genomics enables customers to analyze population-scale genomic data. Starting from the output of our genomics pipeline, this series will provide a tutorial on using Databricks to run sample quality...
Managed MLflow on Databricks now in public preview
Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models. To address these challenges, last June we introduced MLflow, an open source platform to manage the ML lifecycle that works with any machine learning library and environment. The project has grown...
