Given the growing number of AI projects and the complexities associated with bringing these projects to production, and specifically the challenges associated with feature engineering, the industry needs a way to standardize and automate the core of feature engineering. Feature stores provide enterprises with a competitive edge, as they enable them to expedite and simplify the path from lab to production. They enable sharing and re-use of features across teams and projects to save time and effort and ensure consistency across training and inference. Feature stores that enable online feature engineering take it to the next level, as they allow for predictive use cases that require complex calculations happening continuously as fresh data streams in.

In this presentation, we will cover what an online feature store is, how it works and why it provides such an advantage in developing, deploying and monitoring AI in real business environments. We will show how to accelerate the development and deployment of AI applications with automated real-time feature engineering, how this improves accuracy and makes the process more efficient. We will also show how the feature store serves as the heart of the MLOps process, and why glueless integration with training, serving and monitoring frameworks is so important, using various customer examples.

www.iguazio.com

Adi Hirschtein's LinkedIn: https://www.linkedin.com/in/adihirschtein/