Explaining oracles: Techniques to communicate Machine Learning model results

Everyone wants to use Machine Learning and AI. It is modern, shines on resumes and brings great benefits if applied correctly in companies. Yet in many companies adoption is slow or in fits and starts. One of the fundamental factors is that the business units do not quite see the utility or understand the results of the models produced by their Data Science department. In this session we will review how to communicate the results of Machine Learning models so that they are understood and used and not so much from statistics or hyper-complex graphs but of little communicative use.

Pau Sempere

Global AI & Data Science Lead @ Avolta | MVP AI

Elche, Spain


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