Real-time predictions with the azure_ai extension (Preview)

This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.

The azure_ai extension on Azure Database for PostgreSQL now introduces in preview the ability to invoke machine learning models hosted on Azure Machine Learning online endpoints in real-time. 

Azure Machine Learning supports end-end machine learning lifecycles from building and training models to Generative AI workflows with prompt-flow, and these learning models or pipelines can be hosted on fully managed endpoints for inferencing.  Online inferencing endpoints can host pre-trained or custom models that are trained and registered in the Azure Machine learning workspace. They are highly scalable, can handle a large number of requests, serve low latency requirements, have rich traffic monitoring and debugging infrastructure. The ability to invoke these easily from within the database, right where your data resides, helps in building various such as:

• Fraud detection in banking.
• Product recommendations in retail.
• Equipment failure or maintenance in manufacturing.
• Routing in transportation.
• Patient predictions in healthcare.
• Data validation, standardization, classification

For Gen AI models, rich vector support right within the database can store embeddings and do efficient similarity searches.

How to use it

To use the machine learning inference feature:

1. Allow list the azure_ai extension
2. Create the extension in the database you want to invoke inferencing form.
3. Deploy your model to an online inference endpoint and test it.CREATE EXTENSION azure_ai;
4. Configure the extension to point to the Machine Learning inferencing endpoint.
5. Invoke the azure_ml.inference user defined function from within SQL.

Housing Prediction Model Example

We are invoking the azure_ml.inference function, passing input_data that is a jsonb payload for the HTTP request and the online inference endpoint returns jsonb that can be expanded into a set of values depending on the semantics of what the model returns. In this case the model returned housing price predictions in the hundreds of thousands.

You could create a user-defined function for each of the models being invoked which would simplify application interface and allow better visibility into how often this function is called. For example, for the house prediction model invoked above:

Getting Started

To learn more about the azure_ai extension, real-time machine learning inferencing and how it can simplify building applications on Azure Database for PostgreSQL, visit our documentation below:

• Azure AI Extension.
• Azure AI real-time machine learning scoring.
• To learn even more about our Flexible Server managed service, see the Azure Database for PostgreSQL Flexible Server.
• You can always find the latest features added to Flexible Server in this release notes page.

If you have any questions or need guidance, don't hesitate to reach out to us at Ask Azure DB for PostgreSQL.