Video Walkthrough
The Responsible AI Dashboard Learn Module takes about an hour to complete end-to-end. It can take longer if you factor in time for exploration and data entry. You might also run into issues or have questions about what should have happened at a specific point in the lab, to help you debug or understand concepts better.
This no-audio video recording of the learn module can help. The tables below show you the timestamps for each relevant step, within this video. Just visit the YouTube page and open the description to get pre-linked key moments to jump directly to the relevant segments to review walkthrough steps for reference.
1. Prepare Environment
- 00:00:02 | Sign into Microsoft Learn. Launch the Lab
- 00:01:40 | Lab Launched. View VM (left) & Instructions (Right)
- 00:02:19 | Login. Open VS Code. Create Azure ML Workspace
- 00:04:59 | Set env variables. Save config to conflig.json
2. Train Model Locally
- 00:07:22 | Open Notebook #1. Select Kernel, Run All.
- 00:09:21 | ⏳ Takes ~10 mins. Explore Code While You Wait.
- 00:22:24 | Open Azure ML Studio. Verify data registered under "Data"
- 00:27:24 | Open Azure ML Studio. Track build progress under "Jobs"
- 00:28:47 | Open Azure ML Studio, Verify model registry under "Models"
3. Add RAI Dashboard
- 00:28:47 | Open Notebook #2. Select Kernel. Run All.
- 00:30:50 | ⏳ Takes ~10 mins. Explore Code While You Wait.
- 00:32:29 | Task 1: Define the dashboard components
- 00:34:12 | Task 2: Define the job to create the RAI Dashboard Insights
- 00:39:48 | Task 3: Run job to create the RAI Dashboard
- 00:41:29 | Open Azure ML Studio. Monitor "Jobs" Pipeline Progress.
- 00:43:33 | Job Complete. Click "Models". Open RAI Dashboard tab.
5. Debug Your Model
- 00:44:30 | Open Azure ML Studio. Stay in RAI Dashboard Pane"
- 00:44:39 | Task 1: Error Analysis
- 00:50:26 | Task 2: Model Overview
- 00:55:32 | Task 3: Data Analysis
- 00:59:58 | Task 4: Feature Analysis