Patent ID: 11921815
Assignee: ORACLE INTERNATIONAL CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 9:
10. A server system, comprising:
one or more memories storing instructions; and
one or more processors communicatively coupled to the one or more memories to execute the instructions to perform operations to:
receive an input, wherein the input identifies a problem to be solved using a machine-learning application;
select a machine-learning model template from a plurality of templates based at least in part on the input, wherein the machine-learning model template includes metadata, the metadata specifies data expectations and available data formats;
analyze one or more formats of customer data to generate a customer data schema based at least in part on a data ontology that applies to the identified problem;
determine whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template;
based at least in part on the determination that the customer data schema is misaligned with the one or more key features of the selected model, analyze the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations of the selected machine-learning model template as indicated in the metadata;
gather the additional information required to re-align the customer data with the data expectations of the selected machine-learning model template;
based at least in part on the additional information, performing one or more remedial actions on the customer data to re-align with the data expectations of the selected machine-learning model template to result in a transformed dataset, wherein a remedial action of the one or more remedial actions comprises rescaling the customer data to fit the data expectations of the selected machine-learning model template, the rescaling of the customer data includes an infrastructure deployment corresponding to one or more micro services architecture in the transformed dataset; and
train the machine-learning application using the selected machine-learning model template and the transformed dataset.