A technology is described for predicting a launch time for a computing instance. An example method may include receiving a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment. Data associated with launch features of a computing instance may then be obtained, where the launch features may be determined to have an impact on a launch time of the computing instance on a physical host within a computing service environment. The launch features of the computing instance may then be input to a machine learning model that outputs the predicted launch time for launching the computing instance within the computing service environment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory machine readable storage medium having instructions embodied thereon, the instructions when executed by a processor: obtain training data representing launch features for a plurality of previous computing instance launches; train a random forest regression model using the training data; receive a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment; identify launch features associated with launching the computing instance within the computing service environment that have been determined to have an impact on a launch time of the computing instance; and input the launch features associated with launching the computing instance into a machine learning regression model that outputs a predicted launch time for launching the computing instance within the computing service environment. 2. A non-transitory machine readable storage medium as in claim 1, wherein instructions that when executed by the processor further select launch features determined to have an impact on a launch time of a computing instance from launch features including machine image launch features, physical host launch features and customer configuration launch features. 3. A non-transitory machine readable storage medium as in claim 1, wherein instructions that when executed by the processor further determine whether an SLA (Service Level Agreement) launch time is likely to be met by comparing the predicted launch time with the SLA launch time. 4. A computer implemented method, comprising: under control of one or more computer systems configured with executable instructions, receiving a request for a predicted launch time to launch a computing instance on a physical host within a computing service environment; obtaining data associated with launch features of a computing instance that are determined to have an impact on a launch time of the computing instance on a physical host within a computing service environment, using a processor; and inputting the launch features of the computing instance to a machine learning model that outputs a predicted launch time for launching a computing instance within the computing service environment, using the processor. 5. A method as in claim 4, wherein obtaining the data associated with launch features further comprises obtaining data associated with machine image launch features, physical host launch features and customer configuration launch features. 6. A method as in claim 4, further comprising normalizing the launch features prior to inputting the launch features to the machine learning model. 7. A method as in claim 4, further comprising performing a parameter value search for machine learning parameters that result in a goodness-of-fit of the machine learning model to the launch features. 8. A method as in claim 7, wherein performing the parameter value search further comprises, performing a parameter value search for machine learning parameters using a distributed genetic algorithm. 9. A method as in claim 4, wherein obtaining data associated with launch features further comprises, obtaining active training data representing launch features for a plurality of previous computing instance launches; extracting features from the active training data associated with the launch features; and training the machine learning model using the features from the active training data. 10. A method as in claim 4, wherein inputting the launch features to a machine learning model further comprises inputting the launch features to a machine learning model selected from at least one of: a random forest model, an extremely randomized trees model, an AdaBoost model, a stochastic gradient descent model or a support vector machine model. 11. A method as in claim 4, wherein inputting the launch features to a machine learning model further comprises, inputting the launch features of the computing instance to a machine learning regression model. 12. A method as in claim 4, further comprising, receiving a launch request from a customer to launch the computing instance; identifying an SLA launch time associated with a computing instance type for the computing instance; and determining whether the SLA launch time is likely to be met by comparing the predicted launch time for the computing instance with the SLA launch time. 13. A method as in claim 12, further comprising notifying a computing service provider that the SLA launch time is likely not going to be achieved when a determination is made that the predicted launch time is greater than the SLA launch time. 14. A method as in claim 12, further comprising constructing an SLA breach feature for the predicted launch time being greater than the SLA launch time and including the SLA breach feature with other features inputted to a machine learning classification model. 15. A method as in claim 4, further comprising: identifying an SLA launch time for the computing instance; determining whether the SLA launch time is likely to be met by comparing the predicted launch time for the computing instance with the SLA launch time; and analyzing a state of a computing service environment in which the computing instance is to be launched to determine whether an action may be performed that is likely to prevent a breach of the SLA launch time when a determination has been made that the SLA launch time will likely be breached. 16. A method as in claim 15, wherein an action performed further comprises, removing a physical host from a group of physical hosts available to host the computing instance. 17. A method as in claim 15, wherein an action performed further comprises, adding at least one physical host to a group of physical hosts available to host the computing instance. 18. A system comprising: a processor; a memory device including instructions that, when executed by the processor, cause the system to: identifying launch features contained in a launch configuration, the launch features having been determined to have an impact on a launch time of a computing instance within a computing service environment; obtain data for the launch features from a data source; input the launch features to a machine learning model that outputs a predicted launch time for launching a computing instance within the computing service environment; and determine whether an SLA launch time is likely to be met by comparing the predicted launch time with the SLA launch time. 19. A system as in claim 18, wherein the memory device includes instructions that, when executed by the processor, cause the system to input the launch features to a machine learning regression model. 20. A system as in claim 18, wherein the memory device includes instructions that, when executed by the processor, cause the system to obtain: features of a machine image used to launch the computing instance, features of physical host servers capable of hosting the computing instance and features of computing instance attachments that are to be attached to the computing instance when launched.