Organizers: Jason (Jinquan) Dai
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 pre-sent the practice and design tradeoffs for building large-scale deep learning applications (such as computer vision and NLP) for production data and workflow on Big Data platforms. In particular, we will provide an overview of emerging deep learn-ing frameworks for Big Data (e.g., BigDL, TensorFlow-on-Spark, Deep Learning Pipelines for Spark, etc.), present the underlying distributed systems and algorithms, and discuss innovative data analytics + AI application pipelines (with a focus on computer vision models and use cases) for Big Data platforms and workflows.
Schedule:
Motivation
Overview
Analytics Zoo for Spark and BigDL
Distributed Training and Inference
Advanced Applications
Real-World Applications
Q&A
