Guide to deploying AI at the edge with open source
Drivers, challenges, use cases and best practices for edge AI
Artificial intelligence is breaking out of the data centre and moving to the edge. From self-driving cars to industrial assembly lines, the ability to deploy machine learning models on embedded devices and generate real-time, AI-driven insight out in the field offers tremendous potential.
Edge AI enables you to process data locally and take advantage of machine learning in locations where latency, bandwidth or privacy limitations would otherwise make it impossible. Devices can make smarter decisions in real time by circumventing the need to send all the data back to a centralised cloud for processing.
The potential value that edge AI offers is matched by the challenges it poses. Updating models in the field, hardware requirements, data collection and elevated security risks compound the already-high degree of complexity inherent in AI projects.
This guide offers a deep dive into these challenges, and explores how you can address them in a practical way using open source solutions.
Read the whitepaper to learn:
- The key benefits to deploying AI at the edge
- The barriers to successful edge AI projects
- The role of open source technology in edge AI
- How you can simplify edge AI by consuming open source software through a unified solution