Story thread · 2 reports / 2 sources

AI inference: Five best practices for successful AI applications

infoworld.com · 2hShare

While some organizations are still getting started with their AI strategies, others are in pilot purgatory , with few experiments or proofs of concept (POCs) reaching production. Only 25% of organizations have moved 40% or more of their AI experiments into production, according to The State of AI in the Enterprise . We discussed delivering AI proofs of concept that matter at a recent Coffee With Digital Trailblazers on LinkedIn Live. One key reason POCs stumble is when they don’t align well with the AI business strategy or have defined business outcomes. Two other problems: There isn’t a sufficient AI change management program , or employees aren’t involved in the development process. But there’s also a significant technology issue: The architecture used for training AI models and developing AI agents can be very different than what’s used for AI inference, running a trained model to generate outputs in production. Training and inference have very different performance, scalability, co

First report: US labs cut AI inference costs nearly 25% amid price war — cryptobriefing.com, 2d