The disclosure describes one or more implementations of a serverless computing management system that utilizes an online learning model to dynamically adjust the number of serverless execution containers in a serverless pool based on incoming data patterns. For example, for each time instance in a given time period, the serverless computing management system utilizes the online learning model to balance computing latency and computing cost to determine how to intelligently resize the serverless pool, such that the online machine-learning models in the serverless pool can update in a manner that improves accuracy and computing efficiency while also minimizing unnecessary delays. Further, the serverless computing management system provides a framework that facilitates state-based training of online machine-learning models in a stateless and serverless cloud-based environment.