Spark + AI Summit 2019 Product Announcements and Recap. Watch the keynote recordings today!

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Delta Lake, Events, Koalas, MLflow, Product, Spark + AI Summit

Spark + AI Summit 2019, the world’s largest data and machine learning conference for the Apache Spark™ Community, brought nearly 5000 data scientists, engineers, and business leaders to San Francisco’s Moscone Center to find out what’s coming next. Watch the keynote recordings today and learn more about the latest product announcements for Apache Spark, MLflow, […]

Using Dynamic Time Warping and MLflow to Detect Sales Trends

Posted Leave a commentPosted in Apache Spark, Company Blog, Dynamic Time Warping, Education, Engineering Blog, Machine Learning, MLflow, Platform

Try this notebook series in Databricks This blog is part 2 of our two-part series Using Dynamic Time Warping and MLflow to Detect Sales Trends.  The phrase “dynamic time warping,” at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does […]

Introducing MLflow Run Sidebar in Databricks Notebooks

Posted Leave a commentPosted in Announcements, Company Blog, Engineering Blog, Machine Learning, Managed MLflow, MLflow, Platform, Sidebar

At Spark+AI Summit 2019, we announced the GA of Managed MLflow on Databricks in which we take the latest and greatest of open source MLflow and make it easily accessible to all users of Databricks. In that blog post, we promised to build features which bridge Databricks and MLflow concepts to create a seamless integration […]

Announcing General Availability of Managed MLflow on Databricks

Posted Leave a commentPosted in Announcements, Company Blog, Ecosystem, Engineering Blog, Machine Learning, Managed MLflow, MLflow, Platform, Product

Try this tutorial in Databricks MLflow is an open source platform to help manage the complete machine learning lifecycle. With MLflow, data scientists can track and share experiments locally or in the cloud, package and share models across frameworks, and deploy models virtually anywhere. Today at the Spark + AI Summit, we announced the General […]

A Guide to MLflow Talks at Spark + AI Summit 2019

Posted Leave a commentPosted in Company Blog, Events, Machine Learning, MLflow, Open Source, Product, Spark + AI Summit

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 […]

Managing the Complete Machine Learning Lifecycle: On-Demand Webinar now available!

Posted Leave a commentPosted in Company Blog, Data Science, Ecosystem, Education, Machine Learning, Managed MLflow, MLflow, Model Management, Open Source, Product, Webinar

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, […]

MLflow v0.9.0 Features SQL Backend, Projects in Docker, and Customization in Python Models

Posted Leave a commentPosted in Apache Spark, docker, Engineering Blog, Machine Learning, Machine Learning Life Cycle, MLflow, Platform, python, SQLAlchemy

MLflow v0.9.0 was released this week. 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 […]

Managed MLflow on Databricks now in public preview

Posted Leave a commentPosted in Announcements, Company Blog, Data Science, Ecosystem, Engineering Blog, Machine Learning, Managed MLflow, MLflow, Platform, Product

Try this tutorial in Databricks 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 […]

Accelerating Machine Learning on Databricks: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Company Blog, Data Science, Databricks Runtime, Deep Learning, Ecosystem, Engineering Blog, Horovod, HorovodRunner, Keras, Machine Learning, MLflow, Platform, Product, TensorFlow

Try this notebook in Databricks On January 15th, we hosted a live webinar—Accelerating Machine Learning on Databricks—with Adam Conway, VP of Product Management, Machine Learning, at Databricks and Hossein Falaki, Software Development Engineer and Data Scientist at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform […]

Kicking Off 2019 with an MLflow User Survey

Posted Leave a commentPosted in Apache Spark, Ecosystem, Engineering Blog, Machine Learning, MLflow

It’s been six months since we launched MLflow, an open source platform to manage the machine learning (ML) lifecycle, and the project has been moving quickly since then. MLflow fills a role that hasn’t been served well in the open source community so far: managing the development lifecycle for ML, including tracking experiments and metrics, […]