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.




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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