Scalable data strategies for scientific AI-enabled breakthroughs
Artificial Intelligence (AI) has the potential to transform the biopharmaceutical value chain. However, its utility across the enterprise is hampered not by the science but by the data – its quality, quantity, and accessibility. As the volume and complexity of scientific data surge, and with the arrival of generative AI and large language models (LLMs), it has become even more challenging to reduce the gap between raw scientific data and AI-enabled outcomes that are accurate, reliable, and unbiased.
In this webinar, we’ll show how a cloud-native platform, purpose-built for science, and powered by AWS, creates large-scale, liquid data sets that can fuel generative AI models. Migrating raw scientific data to the cloud is insufficient for the meaningful utilization of AI. Only by replatforming and engineering data at scale are biopharma organizations able to deploy accurate LLMs that can radically accelerate and improve scientific outcomes. We’ll also share real-world use cases where generative AI has created novel breakthroughs.
In this webinar you’ll learn how to:
- Assess the generative AI readiness of your data architecture
- Replatform and engineer scientific data in the cloud using a centralized or federated strategy
- Enable rapid consumption of engineered scientific data while optimizing cloud performance, scale, and cost
- Securely develop and deploy generative AI models to derive scientific insights across multiple use cases such as integrated omics, protein binding, and formulations
- Utilize large datasets with LLMs for predictive analyses in toxicology, and in silico ADMET
- Ensure privacy, security, and transparency through generative AI services
