On Demand Video

Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio


Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked. 

Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.

This talk, we will go over:

  • What is Analytics Zoo and how it works
  • How to run Analytics Zoo with Alluxio in deep learning applications
  • Initial performance benchmark results using the Analytics Zoo + Alluxio stack


Jiao (Jennie) Wang is a software engineer on the Machine Learning Platform team at Intel working in the area of big data analytics. She is engaged in developing and optimizing distributed deep learning framework on Apache Spark and provide customer support for end to end AI solutions on big data platforms.

Tsai Louie is working on optimizing of AI Software on Intel architectures. He is currently focused on Deep Learning Software like BigDL, Tensorflow, and MKL-DNN. Prior to joining TCE team, Louie also supported different software performance optimization tasks like Baidu’s autonomous driving project with Intel performance libraries and tools.

Bin Fan is the founding engineer and VP of Open Source at Alluxio, Inc. Prior to Alluxio, he worked for Google to build the next-generation storage infrastructure. Bin received his Ph.D. in Computer Science from Carnegie Mellon University on the design and implementation of distributed systems.

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