Unleash Your Model's Potential: Guide to Deploying a Fabric ML Model on Azure ML Inference Endpoint
In this video, we'll walk through the steps to export a model from Microsoft Fabric and import it into an Azure Machine Learning workspace. We'll look at how to deploy it to a real-time inference endpoint and test it both within Azure ML and from Postman, so you can start leveraging those Azure features right away.
Description
Microsoft Fabric's Data Science workload leverages Synapse ML for training and are ideally suited to enrich data stored in a data lake.
But models developed in Fabric can also be deployed in other environments like Azure Machine Learning to take advantage of real-time inference endpoint deployment and other techniques.
This video walks through the steps to export a model from Fabric, Import it into an Azure ML workspace, and deploy it to a real-time inference endpoint.
This video is a walk-through of the process described in this blog post:
https://robkerr.ai/deploying-fabric-ml-models-to-azure-ml/
Note: Microsoft has inference endpoint support on the Fabric roadmap as of this recording. If you're reading this after Q2/2024 check whether Fabric natively supports inference endpoint deployment as another option to the techniques covered here.
0:00 Introduction
0:49 Export Model from Fabric
2:17 Import Model to Azure ML
3:03 Create a real-time inference endpoint
3:57 Test the endpoint within Azure ML
4:24 Test the endpoint from Postman
6:11 Summary