Understanding edge to core data pipelines to train future AI/ML models

Given that a lot of data is generated at the edge, capturing and transporting the relevant pieces from the edge to a cloud environment is vital to train next-generation models of artificial intelligence and machine learning (AI/ML). Creating a flexible set of data pipelines that allows this transport becomes critical for any organization in this scenario. In this episode, we’ll cover a simple use case, where data collected can be captured, transformed, and transported using an event-driven architecture deployed across edge servers and the cloud environment. Our presenter will cover: - Capturing, transforming, and transporting data across edge servers and the cloud environment to train AI/ML models - Inputting data into a distributed architecture, allowing data engineers to build flexible data pipelines - How Red Hat® OpenShift® provides a platform to build and deploy data pipelines and intelligent applications for AI/ML use cases like Edge analytics - How Red Hat provides architecture design patterns for customers to implement edge to core data pipelines for AI/ML