There is a rarely discussed but tremendously important dynamic facing every business deploying machine learning: what’s changing below the surface.
As companies race towards “AI transformation,” applying machine learning and other techniques to optimize their processes, products, and services, there are numerous simultaneous infrastructure transformations occurring throughout AI stack. These transformations impact both suppliers and adopters and introduce yet more uncertainty to a market already driven by extensive “FOMO, FUD, and feuds.”
Put simply: We’re building the plane as we’re flying the plane
We’re building AI applications as we’re reconstructing the infrastructure and information architecture that will support them.
New configurations are emerging to address core challenges across the stack
The billions of dollars poured into the AI market aren’t just about chasing the bright shiny tech du jour, they are about solving fundamental problems for which current architectures are inadequate or too brittle. Consider just a handful of examples illustrating how innovations across the stack are influencing AI’s development.
Continue reading this post where it was originally posted in InfoWorld where we discuss transformation occurring in the data pipeline itself; in how AI is developed; in how we define edge computing, and why these transformations carry economic and commercial implications.