Cost modelling for AI projects

Most AI/ML projects fail. What can we do in the early stages of our experimentation to minimise the chances that our project doesn't make the mark? In this article, we discuss using Decoded.AI to build effective strategies that maximise our chances of success with less than ten minutes of work.

Our Python SDK

Inspired by the LLVM compiler, the Decoded.AI Python SDK uses a variation of Profile-Guided Optimisation to collect the analytics that we need to help you understand your work in different ways. In our SDK, we use PGO-like strategies to collect interesting data about the typical execution of your Python code that we can turn into insights for you.

What we're about

Right now, the AI industry is going through a transition period where our weights and the patterns that we use to build them are being evaluated in complex social contexts. Most AI projects fail because because we're stuck trying to decide whether and under what conditions a block of code is 'ethical' or 'cost-effective' and that's a significant challenge. At Decoded.AI, we're on a mission to drive AI adoption by making those questions easier to answer.