Time: 9:30 AM Location: CERN, 513-1-024 Location in CERN maps:
Recent breakthroughs in the domain of artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this workshop, we will present the practice and design tradeoffs of building large-scale deep learning applications for production data and workflow on Big Data platforms. We will provide an overview of emerging deep learning frameworks for Big Data, and present the underlying distributed systems and algorithms. And, we will show how to build and productionize deep learning application pipelines for Big Data using Analytics Zoo, which is an open source end-to-end data analytics & AI platform for Apache Spark and BigDL, and we will walk through some of the real-world use cases.
Analytics Zoo provides a unified analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras and BigDL programs into an integrated pipeline that can be transparently scaled out to a large Hadoop/Spark cluster for distributed training or inference. BigDL is a distributed deep learning library for Apache Spark with which users can write deep learning applications as standard Spark programs, which can directly run on existing Spark clusters.