An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A continuous provisioning method, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, the continuous provisioning method comprising: generating, by the one or more computer nodes, a machine learning model; determining, by the one or more computer nodes, whether the machine learning model is a new model; in response to determining the machine learning model is not the new model, retrieving, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model; and determining, by the one or more computer nodes, a difference between the associated model and the new persistent model. 2. The continuous provisioning method of claim 1, wherein the generating the machine learning model comprises: training the machine learning model based on training data, or retrieving data points from a global data stream and updating the machine learning model based on the retrieved data points. 3. The continuous provisioning method of claim 1 further comprising: in response to determining the machine learning model is the new model, generating, by the one or more computer nodes, one or more model containers. 4. The continuous provisioning method of claim 3, wherein the generating one or more model containers comprises partitioning and transforming the new model into the one or more model containers. 5. The continuous provisioning method of claim 1, further comprising patching, by the one or more computer nodes, the determined difference on top of the associated model. 6. The continuous provisioning method of claim 1, further comprising generating, by the one or more computer nodes, one or more model containers by partitioning and transforming the new persistent model into the one or more model containers. 7. The continuous provisioning method of claim 6, further comprising: publishing, by the one or more computer nodes, the one or more generated model containers to a global container registry; and in response to the publishing the one or more generated model containers, deploying, by the one or more computer nodes, the one or more generated model containers to one or more serving nodes. 8. The continuous provisioning method of claim 7, further comprising retrieving, by the one or more serving nodes, at least one image of the one or more generated model containers from a local container registry. 9. The continuous provisioning method of claim 7, further comprising, in response to determining at least one image of the one or more generated model containers is not on a local container registry, retrieving, by the one or more computer nodes, the at least one image of the one or more generated model containers from the global container registry. 10. A continuous provisioning apparatus comprising: a memory and at least one processor configured to execute: a model generator configured to generate a machine learning model; and a model container builder configured to: determine whether the machine learning model is a new model, in response to determining the machine learning model is not the new model, retrieve one or more model containers with an associated model to a new persistent model, and determine a difference between the associated model and the new persistent model. 11. The continuous provisioning apparatus of claim 10, wherein the model generator comprises a model trainer configured to train the machine learning model based on training data, or a model updater configured to retrieve data points from a global data stream and update the machine learning model based on the retrieved data points. 12. The continuous provisioning apparatus of claim 10, wherein the model container builder is further configured to, in response to determining the machine learning model is the new model, generate one or more model containers. 13. The continuous provisioning apparatus of claim 12, wherein the model container builder is further configured to generate the one or more model containers by partitioning and transforming the new model into the one or more model containers. 14. The continuous provisioning apparatus of claim 10, wherein the model container builder is further configured to patch the determined difference on top of the associated model. 15. The continuous provisioning apparatus of claim 10, wherein the model container builder is further configured to generate one or more model containers by partitioning and transforming the new persistent model into the one or more model containers. 16. The continuous provisioning apparatus of claim 15, wherein the model container builder is further configured to publish the one or more generated model containers to a global container registry, and a model container provisioner configured to, in response to the model container builder publishing the one or more generated model containers, deploy the one or more generated model containers to one or more serving nodes. 17. The continuous provisioning apparatus of claim 16, wherein the one or more serving nodes are configured to retrieve at least one image of the one or more generated model containers from a local container registry. 18. The continuous provisioning apparatus of claim 16, wherein the model container provisioner is configured to, in response to determining at least one image of the one or more generated model containers is not on a local container registry, retrieve the at least one image of the one or more generated model containers from the global container registry. 19. A non-transitory computer readable recording medium comprising a program to execute a continuous provisioning method to provide a future prediction in response to a request from one or more client devices, the method comprising: generating, by one or more computer nodes, a machine learning model; determining, by the one or more computer nodes, whether the machine learning model is a new model; in response to determining the machine learning model is not the new model, retrieving, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model; and determining, by the one or more computer nodes, a difference between the associated model and the new persistent model. 20. The non-transitory computer readable recording medium of claim 19, further comprising: in response to determining the machine learning model is the new model, generating, by the one or more computer nodes, one or more model containers by partitioning and transforming the new model into the one or more model containers, and in response to determining the machine learning model is not the new model, generating, by the one or more computer nodes, one or more model containers by partitioning and transforming the new persistent model into the one or more model containers.