Deep Learning for Real Time Streaming Data with Kafka and TensorFlow - ODSC East 2019

Learn how to leverage Apache Kafka and TensorFlow for real-time streaming data analysis in mission-critical applications. Explore the KafkaDataset module in TensorFlow, which processes Kafka streaming data directly into TensorFlow's graph. Discover how this integration eliminates the need for intermediate data processing infrastructure, making machine learning adoption easier for real-time applications. Gain insights into the Python interface of KafkaDataset through TensorFlow's tf.data API and its compatibility with tf.keras. Follow along with a concrete example and demo showcasing the practical implementation of these concepts. Delve into topics such as event-driven microservices, TensorFlow 2.0 workflow, data pipeline performance, and TFRecord data format. Enhance your understanding of deep learning techniques for real-time data processing and analysis.

Intro
Real Time Streaming Data
Event Driven Microservices
Machine Learning
TensorFlow 2.0 Workflow
Motivation of Data Pipeline in ML
Data Pipeline Performance
Data Format Flexibility
TFRecord Data Format
Infrastructure Solution
KafkaDataset (Python/R/C++) in TensorFlow
KafkaDataset in TensorFlow