Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Andres Rodriguez is a deep learning solutions architect with Intel Nervana where he designs deep learning solutions for Intel’s customers and provides technical leadership across Intel for deep learning. Andres received his PhD from Carnegie Mellon University for his research in machine learning, and prior to joining Intel, he was a deep learning research scientist with the Air Force Research Laboratory. He holds over 20 peer reviewed publications in journals and conferences, and a book chapter on machine learning.