Machine Learning is no doubt the hottest trend in IT nowadays. Deep Neural Network (DNN), a subfield of Machine Learning with mode of operation loosely inspired by the brain, allows us to solve complex problems such as image recognition that has been very difficult to solve using standard programming paradigms. DNN concepts are not new. However, and until recently, applying them in practice could not be realized due to their high computational demands. With the recent development in parallel computing, especially around GPU acceleration and high speed and efficient networking, DNN has become a reality in modern data centers. In this talk we will describe the system requirements to effectively run a machine learning cluster with popular frameworks such as TensorFlow. We will discuss how such a system can be deployed in an OpenStack-based cloud without compromises, enjoying high-performance DNN programming paradigm as well as the benefits of cloud and software-defined data centers.
Brain in the cloud: Machine Learning On OpenStack Done Right!
Erez Cohen acts as Mellanox Vice President for CloudX & AI Programs, responsible for all aspects of the programs including strategy, architecture and implementation. The CloudX program span across multiple cloud solutions including OpenStack, Microsoft and VMware and incorporate Mellanox state of the art network and storage interconnect products to form the most efficient and scalable enterprise, scientific, NFV and machine learning cloud infrastructure.
The AI program aims to enable and accelerate state of the art Machine Learning and AI frameworks with Mellanox advance solutions for this space.
Between 2003 and 2013 Mr. Cohen led the Field Engineering group at Mellanox. As part of this role Mr. Cohen was involved with some of the largest and most complex data centers and High Performance Computing clusters in the world.
Between 2000 and 2003 Mr. Cohen lead the Architecture and Design Validation group at Mellanox.
Mr. Cohen holds a Bachelor of Science in Computer Engineering from the Technion Israel Institute of Technology.