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公开(公告)号：US20220318686A1
公开(公告)日：2022-10-06
申请号：US17223796
申请日：2021-04-06
Applicant: SAP SE
Inventor： Nithya Rajagopalan , Panish Ramakrishna , Ashutosh Patel , Ranjith Pavanje Raja Rao , Mayank Kamboj , Arjun Swami
Abstract: In an example embodiment an applications (apps) intelligence framework is utilized to quickly operationalize machine learned models (of different use cases, products, or applications) and take them to production through a set of predetermined pipelines. The app server may include a model configuration component to allow an entity to configure a model for an entity's specific use case. This configuration is then passed to a model generation component in the machine learning component, which acts to generate the specific model for the entity's use case using the configuration. An intelligent scheduling component may then be used to schedule retraining of the specific model at particular intervals. Notably, the intelligent scheduling component is itself a machine learned model (in one example embodiment a neural network) that is trained to dynamically output a training interval for a particular model based on various features.
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2.发明申请公开(公告)号：US20220318687A1
公开(公告)日：2022-10-06
申请号：US17223859
申请日：2021-04-06
Applicant: SAP SE
Inventor： Nithya Rajagopalan , Panish Ramakrishna , Ashutosh Patel , Ranjith Pavanje Raja Rao , Mayank Kamboj , Arjun Swami
Abstract: In an example embodiment, a model generation component may additionally assign various cloud resources to a machine learned model so that the training or retraining of the model can be performed using these resource. The containers may be weighted to handle model generation work of different weight. Having one single configuration for a container responsible for generating all models leads to overuse of hardware resources because machine learning algorithms are very resource intensive, and thus dynamically selecting the weight improves hardware utilization.
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