This talk will peek under the hood to show how we combine deep learning models with biology lab automation at Recursion Pharmaceuticals. Every week, we generate millions of microscopy images of human cells on our high throughput screening platform as part of our search for new drug treatments. Here, I’ll describe how we use machine learning analytics to make sense of all of this complex data - that is, to identify promising drug compounds while simultaneously avoiding high risk ones. As part of our aim to ‘industrialize drug discovery’, we have developed a single, large-volume platform approach that can be applied across large numbers of diseases. I will show how running such a high sophisticated experiment pipeline relies on a close integration of biology, engineering and data science product development: Engineering engineering infrastructure that can handle petabytes of data; Experimental biology setups that are optimized best enable downstream machine learning tasks; deep learning models that work not just on small, pre-defined datasets, but on large quantities of future data. Cumulatively, you will see a vision of medical discovery at an industrial scale.