Optimizing ML Serving with Asynchronous Architectures

When AI architects think about ML Serving, they focus primarily on speeding up the inference function in the Serving layer. When the solution is deployed, the cost of serving alarms those responsible for budgets, leading to abandoning of solutions. The default architecture that architects come up with is a synchronous one. An ML Service API, typical a REST API sits in front of the serving layer. It takes care of standard API functions like authentication and load balancing.