Pocket: ML Serving from the Edge

One of the major challenges in serving ML applications is the resource pressure introduced by the underlying ML frameworks. This becomes a bigger problem at resource-constrained, multi-tenant edge server locations, where it is necessary to scale to a larger number of clients with a fixed resource envelope. Naive approaches which simply minimize the resource budget allocation of each application result in performance degradation that voids the benefits expected from operating at the edge.