|
16 | 16 | - name: Tutorials
|
17 | 17 | expanded: true
|
18 | 18 | items:
|
19 |
| - - name: Studio & Designer |
| 19 | + - name: Studio |
20 | 20 | items:
|
21 |
| - - name: 'Designer: 1. Train a regression model' |
22 |
| - displayName: studio |
23 |
| - href: tutorial-designer-automobile-price-train-score.md |
24 |
| - - name: 'Designer: 2. Deploy that model' |
25 |
| - displayName: studio |
26 |
| - href: tutorial-designer-automobile-price-deploy.md |
| 21 | + - name: Designer (drag-n-drop) |
| 22 | + items: |
| 23 | + - name: '1. Train a regression model' |
| 24 | + displayName: studio |
| 25 | + href: tutorial-designer-automobile-price-train-score.md |
| 26 | + - name: '2. Deploy that model' |
| 27 | + displayName: studio |
| 28 | + href: tutorial-designer-automobile-price-deploy.md |
27 | 29 | - name: Automated ML (UI)
|
28 | 30 | items:
|
29 |
| - - name: Create an automated ML experiment |
| 31 | + - name: Create automated ML experiments |
30 | 32 | displayName: automl, automated, auto ml, portal, ui
|
31 | 33 | href: tutorial-first-experiment-automated-ml.md
|
32 |
| - - name: Forecast demand with automated ML (Bike share data) |
| 34 | + - name: Forecast demand (Bike share data) |
33 | 35 | displayName: automl, automated, auto ml, portal, ui
|
34 | 36 | href: tutorial-automated-ml-forecast.md
|
35 | 37 | - name: Label image data
|
|
81 | 83 | - name: Jupyter Notebooks
|
82 | 84 | displayName: example, examples, server, jupyter, azure notebooks, python, notebook, github
|
83 | 85 | href: samples-notebooks.md
|
84 |
| - - name: Azure Open Datasets |
85 |
| - href: /azure/open-datasets/samples?context=azure/machine-learning/service/context/ml-context |
86 | 86 | - name: Designer datasets
|
87 | 87 | href: sample-designer-datasets.md
|
| 88 | + - name: Designer sample pipelines |
| 89 | + href: samples-designer.md |
88 | 90 | - name: End-to-end MLOps examples
|
89 | 91 | href: https://github.com/microsoft/MLOps
|
90 |
| - - name: Designer pipelines |
91 |
| - href: samples-designer.md |
| 92 | + - name: Open Datasets (public) |
| 93 | + href: /azure/open-datasets/samples?context=azure/machine-learning/service/context/ml-context |
92 | 94 | - name: Concepts
|
93 | 95 | items:
|
94 | 96 | - name: Workspace
|
|
118 | 120 | href: /azure/machine-learning/algorithm-cheat-sheet
|
119 | 121 | - name: How to select algorithms
|
120 | 122 | href: /azure/machine-learning/how-to-select-algorithms
|
121 |
| - - name: Automated machine learning |
| 123 | + - name: Automated ML |
122 | 124 | displayName: automl, auto ml
|
123 | 125 | href: concept-automated-ml.md
|
124 |
| - - name: Manage ML pitfalls |
| 126 | + - name: Overfitting & imbalanced data |
125 | 127 | displayName: automl, auto ml, risks
|
126 | 128 | href: concept-manage-ml-pitfalls.md
|
127 | 129 | - name: Compute instance
|
|
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