Official Azure Reference Architectures for AI workloads
Home Page: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/
License: MIT License
There is one model that need to be daily trained and updated through scheduled pipelines.
The new version of the model will also be updated in existing ACI REST API in staging instance.
Please note we don't want REST API to be created again, as there are applications using existing URL.
Also, if update failed, REST API to retain its state, not error out when called.
How to move this entire scenario to production deployment, so that in production once "deployed", pipelines can run seamlessly and update model and update existing production REST API with new model version?
Please note we don't want REST API to be created again, as there are applications using existing URL. Also, if update failed, REST API to retain its state, not error out when called.
This is a extremely important business requirement and highly common scenario. There is no clear document|tutorial|guidelines around to do this.
Use Case: To read medical test results in XML and pass/fail for a condition
The Results XML will consist of (a) XML elements are constant (in every XML response). E.G. TestNo, Name, Address, DOB etc and (b) dynamic elements e.g. Test1Result, Test2Result etc depending on number of tests carried out.
The values returned need to be compared with Expected Result XML
Dynamically evaluate test result
Ideal test case for COVID test lab.
We want to know of any AIReference Architecture involving some components in another cloud or on-premise
Some of the components like versioning may be required to be in another cloud or on-premise to comply with organization wide policy
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