|
36 | 36 | - name: Train & deploy image classification
|
37 | 37 | displayName: MNIST
|
38 | 38 | href: tutorial-train-deploy-notebook.md
|
39 |
| - - name: Automated ML |
40 |
| - items: |
41 |
| - - name: Object detection with AutoML |
42 |
| - displayName: automl, automated, auto ml, computer vision, images, image model |
43 |
| - href: tutorial-auto-train-image-models.md |
44 |
| - - name: Forecast demand (Bike share data) |
45 |
| - displayName: automl, automated, auto ml, portal, ui |
46 |
| - href: tutorial-automated-ml-forecast.md |
| 39 | + - name: Object detection with AutoML |
| 40 | + displayName: automl, automated, auto ml, computer vision, images, image model |
| 41 | + href: tutorial-auto-train-image-models.md |
47 | 42 | - name: Prep your code for production
|
48 | 43 | displayName: mlops, mlopspython, production
|
49 | 44 | href: tutorial-convert-ml-experiment-to-production.md
|
|
54 | 49 | - name: Create automated ML experiments
|
55 | 50 | displayName: automl, automated, auto ml, portal, ui
|
56 | 51 | href: tutorial-first-experiment-automated-ml.md
|
| 52 | + - name: Forecast demand (Bike share data) |
| 53 | + displayName: automl, automated, auto ml, portal, ui |
| 54 | + href: tutorial-automated-ml-forecast.md |
57 | 55 | - name: Designer (drag-n-drop)
|
58 | 56 | items:
|
59 | 57 | - name: 1. Train a regression model
|
|
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