Life sciences R&D teams face significant challenges with big data, particularly when training deep learning or Foundation Models in biology. Popular single-cell biology models like scGPT and Geneformer require millions of high-quality data points, but the associated computational demands make building scalable, cost-effective infrastructure critical. IT teams must address robust computational power, efficient pipelines, and cost optimization to succeed.
To tackle these challenges, organizations need infrastructure designed to process and annotate data consistently at scale. While platforms like AWS are popular, they often require extensive customization and incur high costs for biology-specific workflows.
In this webinar, we’ll discuss how Elucidata’s domain-specific cloud platform, Polly, outperforms leading solutions like AWS in building and training scRNA-seq-specific Foundation Models.
Life sciences R&D teams face significant challenges with big data, particularly when training deep learning or Foundation Models in biology. Popular single-cell biology models like scGPT and Geneformer require millions of high-quality data points, but the associated computational demands make building scalable, cost-effective infrastructure critical. IT teams must address robust computational power, efficient pipelines, and cost optimization to succeed.
To tackle these challenges, organizations need infrastructure designed to process and annotate data consistently at scale. While platforms like AWS are popular, they often require extensive customization and incur high costs for biology-specific workflows.
In this webinar, we’ll discuss how Elucidata’s domain-specific cloud platform, Polly, outperforms leading solutions like AWS in building and training scRNA-seq-specific Foundation Models.
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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