Advancements in ML, DL, and Generative models have intrigued machine learning practitioners with their potential to revolutionize R&D. However, transitioning from impressive tech demos to production-ready workflows can be challenging.To stay on top, scientists must combine data, infrastructure, models, and subject matter expertise into a formidable base. While the applications are numerous, the foundations are deep and unified. How does one fine-tune? How do we keep trusting the results? How do we deploy?
In this webinar, Swetabh addresses these queries and provide an overview of the necessary tools to set your AI initiatives up for success. Additionally, he demonstrates the winning recipe (of data, models, and infrastructure) through a production-ready Gen AI agent that helps retrieve information across an integrated corpus of genomics, gene expression, and clinical data.
Advancements in ML, DL, and Generative models have intrigued machine learning practitioners with their potential to revolutionize R&D. However, transitioning from impressive tech demos to production-ready workflows can be challenging.To stay on top, scientists must combine data, infrastructure, models, and subject matter expertise into a formidable base. While the applications are numerous, the foundations are deep and unified. How does one fine-tune? How do we keep trusting the results? How do we deploy?
In this webinar, Swetabh addresses these queries and provide an overview of the necessary tools to set your AI initiatives up for success. Additionally, he demonstrates the winning recipe (of data, models, and infrastructure) through a production-ready Gen AI agent that helps retrieve information across an integrated corpus of genomics, gene expression, and clinical data.
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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