Introducing the MLflow Model Registry
At today’s Spark + AI Summit in Amsterdam, we announced the availability of the MLflow Model Registry, a new component in the MLflow open source ML platform. Since we introduced MLflow at Spark+AI Summit 2018, the project has gained more than 140 contributors and 800,000 monthly downloads on PyPI, making MLflow one of the fastest...
A Guide to Women In Unified Analytics Events at Spark+AI Summit Europe
Spark + AI Summit is Europe’s largest data and machine learning conference, and the big news in 2019 is how many women are driving some of the greatest advances in big data, machine learning, and data science. This 15-17 October in Amsterdam, more than 1700 data scientists, engineers, and developers will gather to share best...
A Guide to MLflow Talks at Spark + AI Summit 2019 Europe
We are thrilled to see how well MLflow has been welcomed by the community since we launched it last summer. With now over 800K monthly downloads, 130 code contributors and dozens of contributing organizations including RStudio and Microsoft, it is one of the fastest growing open source projects in the field of Machine Learning, confirming...
A Guide to AI, Data Science, Machine Learning and Deep Learning Talks at Spark+AI Summit Europe 2019
The Spark + AI Summit Europe is just around the corner, and it's a great opportunity for data scientists and Machine Learning (ML) practitioners to get up to speed on the latest tools and innovations in the field! Below is a selection of talks on ML best practices for productionizing ML at scale, real-life use...
AutoML on Databricks: Augmenting Data Science from Data Prep to Operationalization
Thousands of data science jobs are going unfilled today as global demand for the talent greatly outstrips supply. Every day, businesses pay the price of the data scientist shortage in missed opportunities and slow innovation. For organizations to realize the full potential of machine learning, data teams have to build hundreds of predictive models a...
Automated Hyperparameter Tuning, Scaling and Tracking: On-Demand Webinar and FAQs now available!
On June 20th, our team hosted a live webinar—Automated Hyperparameter Tuning, Scaling and Tracking on Databricks—with Joseph Bradley, Software Engineer, and Yifan Cao, Senior Product Manager at Databricks. Automated Machine Learning (AutoML) has received significant interest recently because of its ability to shorten time-to-value for data science teams and maximize the predictive performance of models....
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 of Product Management at Databricks. Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to...
Announcing General Availability of Managed MLflow on 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 Availability of Managed MLflow on...
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...
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,...
