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HomeArtificial Intelligence and Machine LearningWhitepaper: Machine Learning Best Practices in Healthcare and Life Sciences

Whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences

For customers looking to implement a GxP-compliant environment on AWS for artificial intelligence (AI) and machine learning (ML) systems, we have released a new whitepaper: Machine Learning Best Practices in Healthcare and Life Sciences.

This whitepaper provides an overview of security and good ML compliance practices and guidance on building GxP-regulated AI/ML systems using AWS services. We cover the points raised by the FDA discussion paper and Good Machine Learning Practices (GMLP) while also drawing from AWS resources: the whitepaper GxP Systems on AWS and the Machine Learning Lens from the AWS Well-Architected Framework. The whitepaper was developed based on our experience with and feedback from AWS pharmaceutical and medical device customers, as well as AWS partners, who are currently using AWS services to develop ML models.

Healthcare and life sciences (HCLS) customers are adopting AWS AI and ML services faster than ever before, but they also face the following regulatory challenges during implementation:

Building a secure infrastructure that complies with stringent regulatory processes for working on the public cloud and aligning to the FDA framework for AI and ML.
Supporting AI/ML-enabled solutions for GxP workloads covering the following:
Reproducibility
Traceability
Data integrity

Monitoring ML models with respect to various changes to parameters and data.
Handling model uncertainty and confidence calibration.

In our whitepaper, you learn about the following topics:

How AWS approaches ML in a regulated environment and provides guidance on Good Machine Learning Practices using AWS services.
Our organizational approach to security and compliance that supports GxP requirements as part of the shared responsibility model.
How to reproduce the workflow steps, track model and dataset lineage, and establish model governance and traceability.
How to monitor and maintain data integrity and quality checks to detect drifts in data and model quality.
Security and compliance best practices for managing AI/ML models on AWS.
Various AWS services for managing ML models in a regulated environment.

AWS is dedicated to helping you successfully use AWS services in regulated life science environments to accelerate your research, development, and delivery of the next generation of medical, health, and wellness solutions.

Contact us with questions about using AWS services for AI/ML in GxP systems. To learn more about compliance in the cloud, visit AWS Compliance. You can also check out the following resources:

Applying the AWS Shared Responsibility Model to your GxP Solution
Automating GxP compliance in the cloud: Best practices and architecture guidelines
Operational Best Practices for AI and ML
Introducing the Well-Architected Framework for Machine Learning
Machine Learning Lens

About the Authors

Susant Mallick is an Industry specialist and digital evangelist in AWS’ Global Healthcare and Life-Sciences practice. He has over 20+ years of experience in the Life Science industry working with biopharmaceutical and medical device companies across North America, APAC and EMEA regions. He has built many Digital Health Platform and Patient Engagement solutions using Mobile App, AI/ML, IoT and other technologies for customers in various Therapeutic Areas. He holds a B.Tech degree in Electrical Engineering and MBA in Finance. His thought leadership and industry expertise earned many accolades in Pharma industry forums.

Sai Sharanya Nalla is a Sr. Data Scientist at AWS Professional Services. She works with customers to develop and implement AI/ ML and HPC solutions on AWS. In her spare time, she enjoys listening to podcasts and audiobooks, taking long walks, and engaging in outreach activities.

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