1. A system for hybrid approach to natural language understanding, comprising: a machine learning service comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to: create a new natural language understanding model based at least in part by a plurality of user-provided examples; and provide a natural language understanding model dataset comprising at least the new natural language understanding model to other components of the system; an integrated development environment comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to: retrieve the natural language understanding model dataset from the machine learning service; and publish a runtime solution by combining at least a natural language understanding rule and a natural language understanding model dataset; and an interaction engine comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to: retrieve the runtime solution from the integrated development environment; receive a natural language input from an external interface; process the natural language input by at least annotating and classifying the input; generate a log dataset based at least on how the runtime solution and processed input interprets the natural language input; and store the natural language input, and log dataset to a log storage; wherein the processing of the natural language input uses a hybrid solution comprising both natural language rules and machine learning-based classifiers.