Case Study: Incode streamlines GPU orchestration and scales ML operations with ClearML

INCODE streamlines GPU orchestration and manages 10+ TB of data with ClearML

INCODE, a provider of identity verification solutions, faced significant challenges in managing its growing datasets and computational resources. As its team and data scaled into terabytes, the company struggled with inefficient GPU orchestration across cloud and on-premises systems, unreliable testing, and difficulties in maintaining experiment reproducibility without a centralized MLOps platform. This led them to seek a solution from ClearML.

By implementing ClearML's platform, INCODE utilized its GPU orchestration with dynamic agents, AWS Autoscaler, and the Hyperdatasets enterprise data management solution. This provided optimized resource allocation, automatic infrastructure scaling based on demand, and robust version control for their 10+ terabytes of data. The solution streamlined INCODE's entire ML pipeline, eliminating computational bottlenecks, improving workflow efficiency, and enabling effective management of their extensive and complex data collections.

INCODE