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GenAI is as promising as it is challenging. To stay on top, scientists will have to combine data, infrastructure, models, and subject-matter expertise into a formidable base. While Gen AI is incredibly exciting, what does it really take to get these models into production? How can we keep trusting the results? How does one select the right problem? In this session, Swetabh gives us a breakdown of the tools needed to set your GenAI initiatives up for success.
GenAI is as promising as it is challenging. To stay on top, scientists will have to combine data, infrastructure, models, and subject-matter expertise into a formidable base. While Gen AI is incredibly exciting, what does it really take to get these models into production? How can we keep trusting the results? How does one select the right problem? In this session, Swetabh gives us a breakdown of the tools needed to set your GenAI initiatives up for success.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
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