How Bayer transforms Pharma R&D with a cloud-based data science ecosystem using Amazon SageMaker

This article describes how Bayer built a cloud-based Data Science Ecosystem (DSE) on AWS using Amazon SageMaker to transform pharmaceutical R&D operations.

• Bayer faced siloed datasets, multiple data modalities, and inflexible ingestion across clinical, genomic, and real-world evidence data
• Rising R&D costs and disconnected systems hindered ML adoption and cross-domain collaboration
• Selected next-generation SageMaker for multimodal data access, centralized asset marketplace, and integrated tooling
• DSE architecture uses hybrid lakehouse (S3, Iceberg, Redshift) with data mesh principles for decentralized execution
• SageMaker Unified Studio provides unified analytical layer with JupyterLab, MLflow, and AI tools
• Enables cataloging of 300+ TB biomarker data and integration of omics, clinical, chemistry repositories
• Supports 100+ data scientists and 25 high-value ML use cases across organization
• Future enhancements include federated catalogs, semantic layers, and generative AI workflows

Bayer's DSE demonstrates how unified cloud infrastructure accelerates pharmaceutical innovation by breaking data silos and standardizing ML workflows globally.