machinelearning

In AI projects the biggest (and most solvable) source of friction are the handoffs between data scientists, application developers, testers, and infrastructure engineers as the project moves from development to production. This friction exists at every company size, in every industry, and every vertical. Gartner’s research shows that AI/ML projects are rarely deployed in under 9 months despite the use of ready-to-go large language models (LLMs) like Llama, Mistral, and Falcon.

AI/ML is a wildfire of a trend. It’s being integrated into just about every application you can think of. When compared to other technical innovations over the past years, like the blockchain, the infrastructure and tooling was being built (and still is) in parallel to discovering a meaningful application.