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We recently published our ninth AI reference architecture (on the Azure Architecture Center).

 

Reference architectures provide a consistent approach and best practices for a given solution. Each architecture includes recommended practices, along with considerations for scalability, availability, manageability, security, and more. The full array of reference architectures is available on the Azure Architecture Center.

 

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This reference architecture shows recommended practices for tuning the hyperparameters (training parameters) of a scikit-learn Python model. A reference implementation for this architecture is available on GitHub.

 

This architecture consists of several Azure cloud services that scale resources according to need.

 

Topics covered include:

  • Architecture
  • Performance considerations
  • Monitoring and logging considerations
  • Cost considerations
  • Security considerations
  • Deployment

 

See also

Additional related AI reference architectures:

 

Find all our reference architectures here.

 




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