Machine Learning (ML) and Artificial Intelligence (AI) are proven to be important drivers to create corporate success and to efficiently support organizational decision processes. However many projects still tend to fail because of a lack of strategy and how to evolve from model development to deployment. After all, sustainable value will only be realized when models support business processes and decisions in a continuous, systematic way. It should be a core focus for any business, large or small, to develop a so-called factory approach: a repeatable process for bringing ML and AI to life and adopting it into decision making and operational business processes.

This workshop zooms into how a successful Data Science Factory works and how it is operated. Answers are provided to questions like: how should a company organize itself to quickly and successfully operationalize ML/AI models ? how can we define and prioritize the highest value use cases ? which steps are key in the end-to-end ML/AI process ? which profiles need to be involved in which stage ? what is required to inject ML/AI capabilities in a business process ? Once operationalized, how to govern and continuously monitor ML/AI projects ?

Target audience are all roles involved in the Data Science Factory: business leads, AI translators, data science leads, and everybody who is interested in getting business value out of ML/AI.