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.