Accelerating Machine Learning on Databricks Runtime - Databricks

Accelerating Machine Learning on Databricks Runtime

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“We all know the unprecedented potential impact for Machine Learning. But how do you take advantage of the myriad of data and ML tools now available? How do you streamline processes, speed up discovery, share knowledge, and scale up implementations for real-life scenarios?

In this talk, we’ll 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 for Machine Learning, that provides pre-configured Databricks clusters including the most popular ML frameworks and libraries, Conda support, performance optimizations, and more.
– Get started with most popular Deep Learning frameworks within a few minutes and go deep with state of the art model DL diagnostics tools.
– Scale up Deep Learning training workloads from a single machine to large clusters for the most demanding applications using the new HorovodRunner with ease.
– How all of these ML frameworks get exposed to large and distributed data using Databricks Runtime for Machine Learning.”



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About Hossein Falaki

Hossein Falaki is a tech lead at Databricks, working on machine learning infrastructure. Prior to joining Databricks he was a data scientist at Apple’s personal assistant, Siri. He graduated with a Ph.D. in Computer Science from UCLA, and a Masters in Computer Science from University of Waterloo

About Yifan Cao

Yifan Cao is a Senior Product Manager at Databricks. His product area spans ML/DL algorithms and Databricks Runtime for Machine Learning. Prior to Databricks, Yifan worked on two Machine Learning products, applying NLP to find metadata and applying machine learning to predict equipment failures. He helped build the products from ground up to multi-million dollars in ARR. Yifan started his career as a researcher in quantum computing. Yifan received his B.S in UC Berkeley and Master from MIT.