INSTANTIATING MACHINE-LEARNING MODELS AT ON-DEMAND CLOUD-BASED SYSTEMS WITH USER-DEFINED DATASETS

This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model. In additional embodiments, the disclosed system generates a dataset visualization, such as an interactive confusion matrix, for interactively viewing and selecting data generated by the machine-learning model.