Abstract: Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.

Abstract: Manifest files for proxies within an application mesh or service mesh are created using parallel computing. Virtual services in a mesh definition are identified. For each of the virtual services, one or more virtual nodes that receive traffic from the virtual service are identified. For each of the virtual nodes that receive traffic from the virtual service, an edge is added to a first graph. Virtual nodes that send network traffic to at least one of the virtual services are then identified. For each virtual node that sends traffic to the virtual service, an edge is added to a second graph. Then an optimized per node network topology is generated based at least in part on the first graph and the second graph.