Training prediction models and applying machine learning prediction to data is illustrated herein. A prediction instance comprising a set of data and metadata associated with the set of data identifying a prediction type is obtained. The data and metadata are used to determine an entity to train a prediction model using the prediction type. A trained prediction model is obtained from the entity. A notification system may be configured to react to monitor contextual information and apply the prediction. A workflow system may automatically perform a function in a workflow based on prediction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to apply machine learning prediction to data, including instructions that are executable to configure the computer system to perform at least the following: obtain a prediction instance comprising a set of data and metadata associated with the set of data, the metadata including a prediction type; based on the data and metadata determine an entity to train a prediction model using the prediction type; and as a result, obtain a trained prediction model from the entity. 2. The computing system of claim 1, wherein the entity is a remote machine learning service. 3. The computing system of claim 1, wherein the one or more computer-readable media further have stored thereon instructions that are executable by the one or more processors to configure the computer system to monitor user contextual information and proactively apply the prediction model to the prediction instance when contextually relevant and provide contextually relevant suggestions based on the results of applying the prediction model to the prediction instance. 4. The computing system of claim 1, wherein the one or more computer-readable media further have stored thereon instructions that are executable by the one or more processors to configure the computer system to monitor current conditions and automatically perform a function based on application of the prediction model to the prediction instance. 5. The computing system of claim 1, wherein the prediction instance is updated to include a record for prediction, wherein the one or more computer-readable media further have stored thereon instructions that are executable by the one or more processors to configure the computer system to determine that a prediction should be performed locally for the record for prediction using the trained prediction model and the prediction instance. 6. The computing system of claim 1, wherein the entity is a local system. 7. The computing system of claim 6, wherein determining the entity to train the prediction model is performed as a result of determining that a time series in the data is white noise. 8. The computing system of claim 1, wherein the metadata is included in a table with the data or in a side structure separate from a table with the data. 9. A computer implemented method of applying machine learning prediction to data, the method comprising: obtaining a prediction instance comprising a set of data and metadata associated with the set of data, the metadata including a prediction type; based on the data and metadata determining an entity to train a prediction model using the prediction type; and as a result, obtaining a trained prediction model from the entity. 10. The method of claim 9, wherein the entity is a remote machine learning service. 11. The method of claim 9, further comprising monitoring user contextual information and proactively applying the prediction model to the prediction instance when contextually relevant and providing contextually relevant suggestions based on the results of applying the prediction model to the prediction instance. 12. The method of claim 9, further comprising monitoring current conditions and automatically performing a function based on application of the prediction model to the prediction instance. 13. The method of claim 9, wherein the prediction instance is updated to include a record for prediction, the method further comprising determining that a prediction should be performed locally for the record for prediction using the trained prediction model and the prediction instance. 14. The method of claim 9, wherein the entity is a local system. 15. The method of claim 14, wherein determining the entity to train the prediction mode is performed as a result of determining that a time series in the data is white noise. 16. The method of claim 9 wherein the metadata is included in a table with the data or in a side structure separate from a table with the data. 17. A computer system comprising: a machine learning subsystem comprising: a machine learning optimization system configured to obtain a prediction instance comprising a set of data and metadata associated with the set of data, the metadata identifying a prediction type and based on the data and metadata determine an entity to train a prediction model using the prediction type. 18. The computing system of claim 17, further comprising a machine learning notification system configured to monitor user contextual information and proactively apply the prediction model to the prediction instance when contextually relevant and provide contextually relevant suggestions based on the results of applying the prediction model to the prediction instance. 19. The computing system of claim 17, further comprising a machine learning workflow system to monitor current conditions and automatically perform a function based on application of the prediction model to the prediction instance. 20. The computing system of claim 17, further comprising a machine learning prediction subsystem configured to train the prediction model at the machine learning subsystem according to the prediction type and update the prediction instance with the trained prediction model.