How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials

In precision medicine, researchers developing diagnostic tests for early disease detection face a critical challenge: datasets containing thousands of potential biomarkers but only hundreds of patient samples. This curse of dimensionality can determine the success or failure of breakthrough discoveries.

Modern bioinformatics use multiple omic modalities—genomics, lipidomics, proteomics, and metabolomics—to develop early disease detection tests. Researchers in this industry are also often challenged with datasets where features outnumber samples by orders of magnitude. As new modalities are considered, the permutations increase exponentially, making experiment tracking a significant challenge. Additionally, source control and code quality are a mission-critical aspect of the overall machine learning architecture. Without efficient machine learning operations (MLOps) processes in place, this can be overlooked, especially in the early discovery stage of the cycle.

In this post, we explore how Sonrai, a life sciences AI company, partnered with AWS to build a robust MLOps framework using ...