There’s a fundamental problem with the typical model development process: It evaluates models in terms of technical metrics like AUC/precision/recall without also including business metrics like profit/ROI/savings – the stuff that actually matters to the company.

This is a serious problem – if you aren’t measuring business value, you’re not pursuing business value. Further, those technical metrics fail to provide your client/stakeholder meaningful visibility – she doesn’t care about AUC. How is she supposed to authorize deployment?

That’s why Machine Learning Week founder Eric Siegel recently co-founded Gooder AI. It addresses this fundamental issue by way of its SaaS product, the first full-scale platform for machine learning validation – to maximize the value of models by testing and visualizing their business performance.

Spoiler alert: Unlike technical measures, a model’s business performance (profit, savings, etc.) depends on how you use it. So assessing the business value requires a specialized visualization solution, one that allows you to interactively try out what-if deployment scenarios. This includes setting various business inputs, which are subject to change, and moving the decision threshold to estimate the potential deployed value.

Come watch Eric demo Gooder AI and show how it drives ML deployment to maximize business impact.