MLOps for Pharmaceutical Innovation

Artificial intelligence has already demonstrated tremendous impact and potential in the pharmaceutical industry. From accelerating drug discovery, to improving pharmacovigilance, to understanding drug repurposability, the possibilities are seemingly endless. But pharmaceutical companies encounter challenges when trying to scale AI initiatives because of numerous compounding factors. While there are many use cases across R&D, manufacturing and supply chain operations, demand forecasting, compliance, and more, it can be hard to identify the use cases which will generate the maximum business impact. Data science and software development teams experience friction developing systems to support AI solutions at scale, which isn’t unique to the pharmaceutical industry – it’s a challenge everywhere. Pharmaceutical companies lack the infrastructure to support scaled AI applications in production, and data and models remain siloed across organizations. All these factors are further complicated by the rigorous privacy, security, and compliance standards teams must adhere to.

Fortunately, there are steps pharmaceutical companies can take to build enterprise architecture strategies that accelerate AI solution development by incorporating horizontal, repeatable pipelines that address these challenges and lay the foundation for scale. Download the report to learn more.