@@ -12,7 +12,7 @@ metadata:
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ms.author : sgilley
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author : sdgilley
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ms.reviewer : sgilley
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- ms.date : 02/15 /2024
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+ ms.date : 08/21 /2024
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ms.custom : jordan-changes
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# linkListType: architecture | concept | deploy | download | get-started | how-to-guide | learn | overview | quickstart | reference | tutorial | video | whats-new
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@@ -23,6 +23,9 @@ landingContent:
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links :
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- text : " What is Azure Machine Learning?"
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url : overview-what-is-azure-machine-learning.md
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+ - text : " What is Responsible AI?"
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+ url : concept-responsible-ai.md
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+
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# Card
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- title : Setup & quickstart
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linkLists :
@@ -48,6 +51,15 @@ landingContent:
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- text : Set up a reusable pipeline
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url : tutorial-pipeline-python-sdk.md
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+ # Card
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+ - title : " Build AI solutions"
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+ linkLists :
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+ - links :
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+ - text : " What is Azure Machine Learning prompt flow?"
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+ url : overview-what-is-prompt-flow.md
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+ - text : " Get started in prompt flow"
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+ url : get-started-prompt-flow.md
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+
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# Card
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- title : Work with data
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linkLists :
@@ -57,8 +69,6 @@ landingContent:
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url : interactive-data-wrangling-with-apache-spark-azure-ml.md
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- text : Create data assets
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url : how-to-create-data-assets.md
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- - text : Work with tables
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- url : how-to-mltable.md
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# Card
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- title : Train models
@@ -67,14 +77,8 @@ landingContent:
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links :
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- text : Run training with CLI, SDK, or REST API
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url : how-to-train-model.md
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- - text : Tune hyperparameters for model training
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- url : how-to-tune-hyperparameters.md
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- text : Build pipelines from reuseable components
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url : tutorial-pipeline-python-sdk.md
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- - text : Use automated ML in studio
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- url : tutorial-first-experiment-automated-ml.md
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- - text : Train with R
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- url : how-to-r-train-model.md
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# Card
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- title : Deploy models
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linkLists :
@@ -84,10 +88,6 @@ landingContent:
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url : concept-endpoints.md
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- text : Real-time scoring with online endpoints
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url : how-to-deploy-online-endpoints.md
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- - text : Batch scoring with batch endpoints
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- url : batch-inference/how-to-use-batch-endpoint.md
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- - text : Deploy R models
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- url : how-to-r-deploy-r-model.md
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# Card
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- title : " Manage the ML lifecycle (MLOps)"
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