Scaling MLOps in Pharma: Automating Model Deployment and Monitoring with DataRobot on AWS

Abstract

This article discusses the MLOps scaling challenge amidst an increase in medical data and growing regulatory demands, which pharmaceutical companies are facing. On the AWS infrastructure, implementing DataRobot will make it possible to automate model deployment as well as monitoring and maintenance of models — without manual oversight; this traditional bottleneck usually creates another risk avenue for non-compliance with good manufacturing practice requirements. Such solution relevance is driven by an acute need for algorithm reproducibility and traceability under circumstances where pharmaceutical companies concurrently operate dozens of models. A lapse in validation or documentation can lead to clinical program delays and increased costs. The novelty here is a unified automated model registry plus deployment pipelines integrated with built-in drift/accuracy tracking and regulatorily significant audit systems. The major findings are that by automating the lifecycle, artificial intelligence stops being an artisanal collection of disconnected experiments and becomes a manageable production process. It shows how running DataRobot on AWS not only speeds up getting algorithms to the clinic but also makes sure strong FDA and GxP rules are followed with built-in version control, legally applicable report making, and data encryption. Such a strong setup where scientific change and rule order do not fight but instead help each other. The article will be of great use to drug makers, data engineers, MLOps workers, and rule managers who want to see the real use of auto ways for model work.