Machine Learning Custom Skill

By Ashwin Venugopal -

 




Machine Learning Custom Skill

Using a machine learning custom skill works the same as adding any other custom skill to a search index. Using the AmlSkill custom skill is different and you have to explore the considerations of how to effectively use it.

Custom Azure Machine Learning Skill Schema

An Azure Machine Learning (AML) custom skill enriches a search index at the document level. Your document indexer must have an AmlSkill in its skill set. Take note that the custom skill doesn't include settings for batchSize as the AML model will process a single document at a time. The remaining settings that control the performance of the skill are timeout and degreeOfParallelism .

Scaling up the Kubernetes inference cluster suitably to manage your workload is the greatest approach to control the effectiveness of an AML expertise. The findings from the AML model must be stored in a field in the document's index. The results from the custom skill set will then be stored in the field on the document in the index by adding an output field mapping.

Developer tools such as the Python SDK, REST APIs, or Azure CLI can be used to build your Azure Machine Learning model. Using the Azure AI Machine Learning studio, a graphical user interface that enables you to build, train, and implement models without writing any code, is an additional choice.

With a model created, you alter how the scoring code calls the model to allow it to be used by your custom search skill. The last steps are to create a Kubernetes cluster to host an endpoint for your model.

Create an AML Workspace

Azure will generate storage accounts, a key store, and application insights resources when you construct the AML workspace. You can open the Azure AI Machine Learning Studio by clicking on the link in the AML workspace Overview pane.

Conclusion

We have successfully learnt the basics of Machine Learning custom skills.



















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