Building Deep Learning Applications on Big Data Platforms

Speaker

Jason Dai

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Description

Recent breakthroughs in artificial intelligence applications have brought deep learning to the forefront of new generations of data analytics. In this tutorial, we will present the practice and design tradeoffs on building large-scale deep learning applications (such as computer vision and NLP), for production data and workflow on Big Data platforms. We will provide an overview of emerging deep learning frameworks for Big Data (e.g., BigDL, TensorFlowOnSpark, Deep Learning Pipelines for Spark, etc.), and present the underlying distributed systems and algorithms. More importantly, we will show how to build and productionize deep learning application pipelines for Big Data using BigDL (a distribtued deep learning framework for Apache Spark) and Analytics Zoo (an end-to-end data analytics + AI pipeline for Apache Spark and BigDL), using real-world use cases (such as JD.com, MLSListings, World Bank, UnionPay, etc.)

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