The AI Infrastructure Alliance is developing a canonical stack for artificial intelligence and machine learning, bringing together a number of vendors, communities, and other organizations. As businesses seek to bring artificial intelligence and machine learning (AI/ML) into the mainstream, challenges emerge. Scaling from pilot projects to production can be difficult. Earlier this year, the issue got new attention and a group that seeks to address the problems got noticed. "Band of AI startups launch'rebel alliance' for interoperability" That headline in VentureBeat certainly caught my eye.

In an effort to create a standard set of tools that would help data science teams collaborate on AI development, an infrastructure initiative launched this week will promote a unified stack for developing and scaling machine learning models. The AI Infrastructure Alliance said this week it will initially focus on creating Canonical Stack for AI envisioned as a development platform for machine learning models destined for enterprise applications. As with previous hardware and software stacks, the machine learning initiative seeks to forge an AI development infrastructure that would free developers to address more complex problems. As machine learning models move to the edge, the alliance said it would create a single platform that integrates existing AI technologies into a common framework that would accelerate and improve MLOps and edge applications. Establishing a so-called canonical AI stack for machine learning and MLOps would include developing best practices and architectures used to scale machine learning models in edge and other applications.