Abstract: A system provides an application programming interface to initiate a request to provide predictions based on a dataset. In response to the request, the system analyzes the dataset and selects, based on the analysis, one or more types of predictive models to use to make predictions. The system trains the predictive models using the dataset, and uses the predictive models to make predictions. In response to an additional dataset, the system replaces one or more of the predictive models using different model types, based on analysis of the additional dataset.

Abstract: Approaches presented herein can allocate resources in such a way that sufficient capacity will be provided to perform a job or task, while minimizing any excess capacity included with those allocated resources. A number of jobs can be performed with differently sized resource instances in some embodiments, to determine an instance size, from a set of available sizes, that is appropriate for each of those jobs. Various parameters for those jobs can be determined, and those values associated with the determined instance sizes. When a new job is received that is to be performed, the parameter values for that job can be compared against the corresponding values for these testing jobs, and an instance size can be selected where a testing job that was successfully performed with that instance size had the same or larger values for these parameters.

Abstract: Monitoring may be performed for time series prediction models. Data to generate a new time series forecast may be received. A determination may be made that the data is associated with a previously generated time series forecast by a machine learning model. Performance metrics may be generated for the machine learning model according to a comparison of the data with the previously generated time series forecast. The performance metrics can then be provided for further analysis and action.