Sanofi Digital Accelerator uses AWS SageMaker for predictive AI and deploys Bedrock for generative AI pilots
Sanofi launched its Digital Accelerator to speed up digital innovation across research and development, clinical, commercial, and manufacturing workflows. The initiative aims to shorten the time from discovery to therapy and improve patient, provider, and employee experiences through AI-powered solutions.
Reported outcomes
24 hours
timeTime & speed
Strategic outcomes
Primary read
Use case focus
Showing 3 of 4
- 1Predictive maintenance and manufacturing efficiency
- 2Drug discovery acceleration
- 3Clinical trial efficiency
- Reduce the time from data discovery to therapy.
- Improve efficiency across R&D, clinical, commercial, and manufacturing functions.
- Build and operationalize AI and machine learning capabilities quickly in a complex global pharmaceutical organization.
- Sanofi standardized on AWS as the foundation for its Digital Accelerator and used a small agile pod model with product owners, scrum masters, engineers, data scientists, and business partners.
- The team uses Amazon SageMaker to build, train, and deploy machine learning models and uses Amazon SageMaker Studio as a single web-based environment for end-to-end ML development.
- The AWS stack described in the article also includes Amazon S3, AWS Lambda, and Amazon API Gateway for data storage, serverless compute, and API delivery.
- Sanofi explored generative AI with Amazon Bedrock, ran a hackathon to evaluate more than 10 projects, and deployed three models for employee productivity, streamlined business processes, and medical-legal content review automation.
- Sanofi introduced eight products in 18 months through the Digital Accelerator.
- Advanced analytics processes were reduced from 6 months to 1 month.
- The accelerator enabled delivery of full API sets in less than 24 hours according to the article.
- More than 300 new talents joined Sanofi's digital, data, and cybersecurity teams.
Architecture
The article describes an AWS-centered serverless and ML stack built around Amazon SageMaker and Amazon SageMaker Studio for model development, with Amazon S3 for storage, AWS Lambda for event-driven compute, and Amazon API Gateway for API management. Sanofi also evaluated and deployed generative AI pilots on Amazon Bedrock.
Sources & evidence1
AI-generated summary. Verify important details with the linked sources before relying on this case.