Hosted By: Phil Darringer, Director, Product Management, Cloud and Machine Learning, Kinetica
Innovative organizations are looking to take their AI projects out of the lab and into production. Until now, this demanded several different platforms and significant data movement, making it a complex, difficult process. In this session, we’ll cover how Kinetica supports the machine learning lifecycle, from exploration to training, inferencing to AI-powered applications.
IN THIS SESSION, WE’LL COVER:
- A demo of how data scientists can work with their favorite notebooks and Kinetica tools to explore huge data sets
- How to leverage GPU acceleration for fast model training
- Orchestrating data pipeline automation on deployment
- How to audit AI to improve accuracy
