Operationalizing Predictive Models with Azure ML

Mature digital transformation requires that the output of predictive models is automatically incorporated in your business systems. This is accomplished by operationalizing your predictive models. In order to do this, your models must be deployed in a robust and scalable environment like Azure ML.

Azure ML will allow you to create an API interface to your models which is then integrated into your existing systems for real-time scoring and predictions. Azure ML also supports the ability to monitor the models for continued accuracy and reliability. If the model degrades, you can retrain the model to make sure you are getting viable outcomes.

In this video Dunn Solutions will demonstrate how to operationalize your predictive models by using the example of a recommendation engine. This includes:

1) Implementing and deploying a web service in Azure Machine Learning
2) Monitoring and retraining predictive models
3) Scalability and performance

For more information, please visit: www.dunnsolutions.com

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Видео Operationalizing Predictive Models with Azure ML канала DunnSolutions