Introduction by EASA: This is the approach taken by our technology partners Front End Analytics in developing Predictive Analytics 3.0. It’s a fascinating proposition – and involves using conventional analytical tools to “inform” a machine learning model, thus dramatically reducing the amount of data required to train the ML algorithm. The whole thing is “democratized” – that is, deployed to end-users – with EASA, a model-agnostic deployment platform which enables companies to safely share and deploy all kinds of models, from financial models in spreadsheets to engineering models in Matlab – and now, Machine Learning models created in TensorFlow and other frameworks.
You can read the paper here. The application cited in the paper is the prediction of failure of an automotive component, but this approach has applications not only in manufacturing, but also in areas such as drug design, financial analysis and risk management, healthcare, and many more.