Skip to content

Latest commit

 

History

History
17 lines (10 loc) · 1.3 KB

Lab10A.md

File metadata and controls

17 lines (10 loc) · 1.3 KB
Raw

Lab 10A: Monitoring a Model with Application Insights

When you deploy a model as a service, it's useful to be able to track information about the requests it processes.

Before You Start

Before you start this lab, ensure that you have completed Lab 1A and Lab 1B, which include tasks to create the Azure Machine Learning workspace and other resources used in this lab.

Task 1: Monitor a Model with Application Insights

In this task, you'll deploy a model as a service, and use Azure Application Insights to monitor it.

  1. In Azure Machine Learning studio, view the Compute page for your workspace; and on the Compute Instances tab, ensure your compute instance is running. If not, start it.
  2. When the compute instance is running, click the Jupyter link to open the Jupyter home page in a new browser tab.
  3. In the Jupyter home page, in the Users/DP100 folder, open the 10A - Monitoring a Model.ipynb notebook. Then read the notes in the notebook, running each code cell in turn.

Note: If you intend to continue straight to the next exercise, leave your compute instance running. If you're taking a break, you might want to close all Jupyter tabs and Stop your compute instance to avoid incurring unnecessary costs.