Leveraging Computer Vision, Sound Wave Recognition, Data Streaming, Machine Learning, and AI to Identify Anomalies in Real Time
Edge analytics continues to gain traction for addressing a wide range of use cases. Among the most common are fast anomaly detection and error classification enabling immediate action on a particular problem.
Watch this session on how to maximize the value of real-time edge analytics using technologies such as sound wave recognition, data streaming, machine learning, and AI. We will show how to train and execute an AI model that mimics an expert human in identifying device issues by listening to machine sounds. We cover, in detail, how TIBCO technology, with the power of open source developments, can deliver end-to-end edge analytics solutions.
You will learn:
- How to use Project AIR from TIBCO LABS as an edge analytics platform
- How to incorporate sound wave analysis for real-time anomaly detection
- How to use Project AIR to deploy and manage machine learning models for real-time analytics