Regression algorithms are tools used to predict continuous numerical values, like as prices, temperatures, or sales numbers, by learning the mathematical relationship between features and targets. A model that appears to work is easy to build. Most of the effort in credible machine learning goes into establishing whether the appearance…
Regression algorithms are tools used to predict continuous numerical values, like as prices, temperatures, or sales numbers, by learning the mathematical relationship between features and targets. The purpose of a first ML model is not to prove that ML works. It is to determine whether the available data contain enough…
A number means nothing without something to compare it against. Previous blogs were about interrogating data before modelling it. This one is about interrogating a result before believing it, and the tool for that is always a comparison: against a deliberately stupid model, against measurement uncertainty, against what a chart…
Ten dimensions, each scored 1–5 against defined anchors. Score a named dataset for a named use case, not a dataset in the abstract: the same historian tags may be ready for shift-level yield forecasting and hopelessly unready for per-part defect prediction. These feed into key data management…
The dataset is the deliverable that determines everything downstream. No model recovers from bad data, and almost every serious industrial ML failure is a data failure that was discovered too late. A model can learn a bad measurement just as easily as a good one. To evaluate how ready for…
Here's a template for an ML opportunity charter. This is done once the opportunity map leads us to decide which one is the first model to build; learn more here. Project title A clear working name. Manufacturing problem What currently happens, and why is it a problem? Decision…
The potential applications for machine learning (ML) and artificial intelligence (AI) appear limitless, making it challenging to determine where to begin amidst all the hype surrounding these technologies. And the challenge is real; defining the problem to solve is where all ML journeys begin. This blog will outline some key…
My leverage in AI/ML implementation comes from problem framing, data strategy, domain physics, and the economics of the operating point. Roughly 70% of the value in an industrial ML project sits in these areas. Coding skill exists to let me audit the remaining 30% credibly, not to let me…