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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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