A schema representing an iterative cycle with 5 steps: "product vision" -> "accuracy estimate" -> "training & evaluation" -> "labelled data" -> "annotation scheme" -> back to "product vision"

How to maximize probability of success for your Machine Learning solution?

Throughout my 15 years as data scientist in academia, big pharma and through consulting, one common theme has emerged: the most reliable predictor of success for any NLP or ML-based solution is whether or not you involve the data science team early on. By introducing your data scientists to the domain experts right from the start of the project, you can iteratively refine and improve both your data and your ML models.

→  Full article: LinkedIn