Home > Workload Solutions > High Performance Computing > White Papers > Running ML/DL Workloads Using Red Hat OpenShift Container Platform v3.11 > Business case
Deep learning (DL) has demonstrated success in many application domains, including computer vision, speech recognition, and natural language processing. Despite the widespread adoption of DL, model development, training, and management at scale still pose significant engineering challenges. Enterprises are investing in custom infrastructure platforms to support their Artificial Intelligence (AI) use cases and the computing needs of their data science teams, often using ad hoc hardware implementations that are outside mainstream data center systems infrastructure. The ability to integrate production-grade, experimental AI technologies in well-defined platforms facilitates wider adoption.