AI is becoming more prevalent for applications like smart medical devices, robotics, and financial trading, where predictions are made in near real-time. However, there are many complexities associated with managing high-frequency data, the AI modelling approach, and integrating the models in a streaming architecture.
This talk will focus on building a system to address these challenges through:
- Using a stream processing framework that incorporates time-windowing and manages out-of-order data with Apache Kafka
- Synchronizing data and managing mathematical assumptions with signal processing techniques
- Incorporating time requirements when choosing and implementing AI models
- Updating and caching the models in the system
During the session, we will walk through the process of performing signal processing and time-alignment and designing and deploying a machine learning algorithm for streaming data.
Heather Gorr, PhD
MATLAB Product Manager, Data Science
10:20AM - Day 1