On-Demand Webinar
We all know it: the potential for Machine Learning practitioners to make a breakthrough and drive positive outcomes is unprecedented. But how do you take advantage of the myriad of data and ML tools now available at your fingertips? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios?

Databricks Unified Analytics Platform is a cloud-service that provides you with ready-to-use clusters to handle all analytics processes in one place, from data preparation to model building and serving, with virtually no limit to how much you can scale.

In this webinar, we will cover some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine Learning. In particular we will show you how to:

  • Get started quickly using the Databricks Runtime 5.0 for Machine Learning, that provides a pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more.
  • Track, tune, and manage models, from experimentation to production, with MLflow, an open-source framework for the end-to-end Machine Learning lifecycle that allows data scientists to track experiments, share and reuse projects, and deploy models quickly, locally or in the cloud.
  • Scale up deep learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner.

Adam Conway
VP Machine Learning, Databricks                                   
Adam is VP of Product Management for Machine Learning at Databricks. He founded the Donkeycar project, an open source self driving RC car, in addition Adam helps organize the DIYRobocars Races in Oakland. Previously Adam helped found Aerohive a now publicly traded WiFi company. He graduated with a Masters in Engineering from Stanford University.     

Hossein Falaki
Software Engineer, Databricks                                          
Hossein Falaki is a software engineer and data scientist at Databricks, working on the next big thing. Prior to that he was a data scientist at Apple’s personal assistant, Siri. He graduated with a Ph.D. in Computer Science from UCLA, where he was a member of the Center for Embedded Networked Sensing (CENS).

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