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The Applied Machine Learning Group (AML) strives for the application of a data-driven approach to drug research and development by developing and utilizing best-in-class machine learning (ML) models to optimize R&D processes. The implementation of ML algorithms accelerates drug discovery and a platform to monitor and self-train models enables portfolio view of our capabilities, allowing us to become more focused. We envision to work in a fast-paced setting, we expect to fail quickly and adapt swiftly, all while expanding your ML capabilities. We also believe in making the right choices not only for our immediate team but decisions to build solutions catered to broader R&D. The success at AML will be measured by bucketing the benefit of the developed capability into one or more of these three buckets, (1) Savings in time (2) Savings in cost (3) Generation of new insights

At the Fiesta de las funciones, we celebrated DS&AI's teams, sub-teams and projects in a dynamic, interactive poster session where everyone presented their work, and attendees could collect a POAP (Proof of Attendance Protocol) for each poster they visited, 20 posters and 21 POAPs. The POAPs served as a digital certificate of attendance and allowed attendees to gamify their participation in the event while getting to know a new digital technology. Utility was given to the person that collected the most POAPs, in the form of a prize given at the end of the event. Additionally, at the end of the poster session, there was a quiz that allowed attendees to test their knowledge of the presented posters. This quiz was a fun way to encourage attendees to interact with the posters and learn more about the various data science, ML and AI work currently going on in DS&AI. It was a great opportunity for Bayer DS&AI colleagues to interact and get to know each other and what we do, and to set the foundations for future collaborations.