DescriptionHigh Performance Computing (HPC) and Deep Learning (DL) share many characteristics: intensive computing, large datasets, and need for easy access to clustered resources by end-users. We predict that DL usage generalization will boost HPC market growth. Conversely, HPC community experience and the large installed base of HPC infrastructures will boost DL growth. Both HPC and DL are evolving together to the as-a-service model by reusing and adapting matured HPC and Cloud concepts such as massive scalability, resource managers, containers, orchestration mechanisms, GPU Computing, batch schedulers, HPC-as-a-Service software, HTTP RESTful-APIs, and web user interfaces. Data scientists need a high-level DL API and a web user interface that both hide HPC systems’ complexity. We will illustrate our move from HPCaaS to DLaaS by showing how we manage DL training tasks and frameworks on standard HPC clusters through a web user interface. Pros and cons of possible architecture choices will be discussed.