Being able to monitor and analyze an Azure Machine Learning web service is crucial to determining the correctness of the server. Azure Machine Learning Studio provides the tools required to perform this monitoring and analysis.
In this course, you'll learn how application insights can be used to monitor an Azure Machine Learning web service, as well as to capture and review telemetry data. Next, you'll examine how to create a data drift monitor and schedule it to run using Jupyter Notebook and Python. You'll explore problems relating to data privacy and how differential privacy works. Finally, you'll learn how to use SmartNoise to generate and submit differentially private queries.
This course is one in a collection that prepares learners for the Designing and Implementing a Data Science Solution on Azure (DP-100) exam.
Learning Objectives
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