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.
- Microsoft Data Science Virtual Machine (DSVM)
- Azure Machine Learning service
- Azure Machine Learning Compute
- Azure Container Registry
- Azure Blob
Topics covered include:
- Architecture
- Performance considerations
- Monitoring and logging considerations
- Cost considerations
- Security considerations
- Deployment
See also
Additional related AI reference architectures:
- Batch scoring of Spark models on Azure Databricks
- Distributed training of deep learning models on Azure
- Batch scoring on Azure for deep learning models
- Batch scoring of Python models on Azure
- Real-time scoring of Python Scikit-Learn and deep learning models on Azure
- Real-time scoring of R machine learning models
- Enterprise-grade conversational bot
- Build a real-time recommendation API on Azure
Find all our reference architectures here.
AzureCAT Guidance
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