Building Deep Learning Applications for Big Data
An Introduction to Analytics Zoo: Distributed TensorFlow, Keras and BigDL on Apache Spark
Speaker
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 Apache Spark, etc.), and present the underlying distributed systems and algorithms. More importantly, we will show how to build and productionize end-to-end deep learning application pipelines for Big Data (on top of Analytics Zoo, a unified analytics + AI platform for distributed TensorFlow, Keras and BigDL on Apache Spark), using real-world use cases (such as Azure, JD.com, World Bank, Midea/KUKA, etc.)
Schedule
Sunday, January 27 (1:30PM - 5:30PM), 2019
| 1:30PM - 1:45PM | Motivation |
| 1:45PM - 2:15PM | DL frameworks on Apache Spark |
| 2:15PM - 2:45PM | Analytics Zoo Overview |
| 2:45PM - 3:15PM | Analytics Zoo Examples |
| 3:15PM - 3:45PM | Break |
| 3:45PM - 4:15PM | Distributed training |
| 4:15PM - 4:35PM | Advanced applications |
| 4:35PM - 5:20PM | Real-world applications |
| 5:20PM - 5:30PM | Q&A |