Streamlining NLP Model Creation and Inference

At Primer we deliver applications with cutting-edge NLP models to surface actionable information from vast stores of unstructured text. The size of these models and our applicationsÕ latency requirements create an operational challenge of deploying a model as a service. Furthermore, creation/customization of these models for our customers is difficult as model training requires the procurement, setup, and use of specialized hardware and software. PrimerÕs ML Platform team solved both of these problems, model training and serving, by creating Kubernetes operators. In this talk we will discuss why we chose the Kubernetes operator pattern to solve these problems and how the operators are designed.