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Azure Machine Learning Studio classic provides an interactive canvas that allows you to develop, run, test, and iterate an experiment representing a predictive analysis model. There are a wide variety of modules available that can:. Once you're satisfied with your experiment, you can deploy it as a Classic Azure Machine Learning Web service or a New Azure Machine Learning Web service so that users can send it new data and receive back results. In this article, we give an overview of the mechanics of how your Machine Learning model progresses from a development experiment to an operationalized Web service. There are other ways to develop and deploy machine learning models, but this article is focused on how you use Machine Learning Studio classic. While Azure Machine Learning Studio classic is designed to help you develop and deploy a predictive analysis model , it's possible to use Studio classic to develop an experiment that doesn't include a predictive analysis model.
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Video Indexer VI , the AI service for Azure Media Services enables the customization of language models by allowing customers to upload examples of sentences or words belonging to the vocabulary of their specific use case. Since speech recognition can sometimes be tricky, VI enables you to train and adapt the models for your specific domain. Harnessing this capability allows organizations to improve the accuracy of the Video Indexer generated transcriptions in their accounts.
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In this tutorial, you take an extended look at the process of developing a predictive analytics solution. You develop a simple model in Machine Learning Studio classic. You then deploy the model as an Azure Machine Learning web service. This deployed model can make predictions using new data.
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