Microsoft patents system to predict cloud server needs using AI

1. A device for increasing worker instance count for an availability zone in a cloud-based computing platform, comprising: one or more memories storing instructions; and one or more processors coupled to the one or more memories and configured to execute the instructions to: provide resource allocation information as input to a machine learning (ML) model to receive an output of a time series forecast of a workload for the availability zone in a future time period, wherein the resource allocation information comprises a history of resource allocation requests for the availability zone including original allocation requests, retry requests that occur when an original allocation request fails, and requests related to underlying platform issues, wherein the ML model is trained on a timespan of workload history for the availability zone such that the time series forecast weighs workload increases due to customer demand over workload increases due to system disruption; compute a throughput threshold for the worker instances in the availability zone by creating throughput bins from a history of workload data and identifying a throughput bin value at which a number of throttling or timeout exceptions is at least a percentage; compute a predicted number of worker instances in the availability zone for handling the workload in the future time period based at least in part on the outputted time series forecast and the computed throughput threshold; when a number of worker instances in the availability zone is less than the predicted number of worker instances, provision a recommended number of servers for the number of worker instances in the availability zone, and process the workload with the provisioned servers at the future time period.