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6 | 6 | - name: What is Azure Machine Learning service?
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7 | 7 | displayName: AML, services, overview, introduction
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8 | 8 | href: overview-what-is-azure-ml.md
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9 |
| - - name: Architecture & terms |
| 9 | + - name: Architecture & terms |
10 | 10 | displayName: architecture, concepts, definitions, glossary
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11 | 11 | href: concept-azure-machine-learning-architecture.md
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12 | 12 |
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29 | 29 | href: tutorial-deploy-models-with-aml.md
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30 | 30 | - name: Regression (NYC Taxi data)
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31 | 31 | items:
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32 |
| - - name: 1. Prepare data for modeling |
33 |
| - displayName: data prep |
34 |
| - href: tutorial-data-prep.md |
35 |
| - - name: 2. Auto-train an ML model |
| 32 | + - name: Auto-train an ML model |
36 | 33 | displayName: automl, automated, auto ml,
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37 | 34 | href: tutorial-auto-train-models.md
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38 | 35 | - name: Visual interface
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|
131 | 128 | href: how-to-configure-environment.md
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132 | 129 | - name: Manage software environment
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133 | 130 | displayName: pip, Conda
|
134 |
| - href: how-to-use-environments.md |
| 131 | + href: how-to-use-environments.md |
135 | 132 | - name: Enable logging
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136 | 133 | displayName: troubleshoot, log, files, tracing
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137 | 134 | href: how-to-enable-logging.md
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|
208 | 205 | displayName: time series
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209 | 206 | href: how-to-auto-train-forecast.md
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210 | 207 | - name: Understand charts and metrics
|
211 |
| - href: how-to-understand-automated-ml.md |
| 208 | + href: how-to-understand-automated-ml.md |
212 | 209 | - name: Deploy & serve models
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213 | 210 | items:
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214 | 211 | - name: Where and how to deploy
|
|
228 | 225 | - name: Deploy to Notebook VMs
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229 | 226 | href: how-to-deploy-local-container-notebook-vm.md
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230 | 227 | - name: Use custom Docker image
|
231 |
| - href: how-to-deploy-custom-docker-image.md |
| 228 | + href: how-to-deploy-custom-docker-image.md |
232 | 229 | - name: Deploy existing models
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233 | 230 | displayName: publish existing model
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234 | 231 | href: how-to-deploy-existing-model.md
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