Azure Data Scientist Associate: Machine Learning Model Monitoring

Azure    |    Intermediate
  • 8 videos | 49m
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 7 users Rating 4.0 of 7 users (7)
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.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Describe how application insights can be used to monitor an azure machine learning web service and capture and review telemetry data
    Monitor a model that is deployed as an azure machine learning real-time service using jupyter notebook and python
    Create a data drift monitor and the schedule to run it
  • Use ml studio to visualize data drift
    Describe data privacy problems and how differential privacy works
    Use smartnoise to generate and submit differentially private queries
    Summarize the key concepts covered in this course

IN THIS COURSE

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