How biotech ML teams scale reproducible, auditable workflows as data and compute demands grow

Description

AI-native biotech teams have made machine learning central to how they do discovery and diagnostics, and the work behind it keeps getting heavier: terabyte imaging, multi-step genomics, molecular simulation, GPU-heavy training, and experiments that can take days or months to finish. More of it runs on its own now, with longer chains of steps and less hands-on supervision. Most teams start with their own scripts or a basic scheduler, and that works for a while. Then something shifts. A new data type, a bigger team after a raise, or the first real conversation with regulators. Suddenly you can't re-run an experiment from six months ago without guessing at what changed. Compute bills go up and no one can explain where the money went. The audit trail that used to be fine isn't anymore, and for teams working with patient data, privacy and residency rules like HIPAA and GDPR start shaping how the infrastructure gets built. The team has to decide whether to keep extending what they have, standardize on a platform, or rethink the architecture. This roundtable puts ML and engineering leaders from AI-native biotech and life-sciences companies in the same room to talk through that shift, from reproducibility and compute cost to compliance and keeping research teams focused on the science as the work scales.

Speakers

Jasmin Bharadiya

Senior Data Engineer, Character Bio