Self-hosting & scaling models

Edge AI

Deploying AI models at the edge presents unique challenges and opportunities. Tuhin notes the growing interest in edge AI, though few models are in production yet. He highlights the complexity of adapting models to diverse devices, emphasizing the need for generalization at the OS layer 1. Chris Benson adds that the field is ripe for innovation, with millions of devices awaiting AI capabilities 1.

There's a lot of research going on and no one. It's not a solved problem and one, you know, there's not a set of best practices yet.

--- Chris Benson

Daniel Whitenack mentions the shift towards Internet-connected devices, which could simplify model optimization and deployment 1.

   

AI Evolution

The evolution of AI has been rapid and transformative, especially with the rise of generative AI. Tuhin reflects on the shift from small models to larger ones, noting that every engineer now needs to engage with machine learning 2. Chris Benson observes the industry's maturation, highlighting the widespread adoption of AI technologies in products 2.

It's amazing how far we've come in such a short time.

--- Chris Benson

The integration of AI into nearly every product marks a significant industry shift, with machine learning becoming a fundamental component of new developments 3.