Background
Relation Therapeutics adopted a standardized MLOps framework on Amazon SageMaker to streamline ML workflows, enhance model governance, and accelerate scientific discovery with scalable, secure, and reproducible infrastructure.
The Challenge
Relation Therapeutics needed to modernize and scale its machine learning workflows to accelerate drug discovery while maintaining governance and reproducibility.
- Fragmented ML workflows built on ad-hoc scripts and unmanaged infrastructure slowed research progress.
- Manual processes across data preprocessing, model training, and deployment led to inefficiencies and reproducibility challenges.
- Lack of standardized governance limited auditability, model traceability, and regulatory compliance.
- Collaboration across research teams was hindered by inconsistent tooling and access controls.
Our Solution
A standardized MLOps framework on SageMaker was proposed to automate and govern Trendsapio’s ML workflows:
