<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Workspace advice for ML tasks in Data Science</title>
    <link>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4750127#M799</link>
    <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I need some advice/example use case for using ML in Fabric.&lt;/P&gt;&lt;P&gt;We have a Dev workspace containing artifacts for ingesting data, storing data, cleaning &amp;amp; feature engineering, model training &amp;amp; logging experiments, and finally the best model is selected from the experiment and saved as a new version of the model.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The problem is ML models are the only artifacts that can't be directly deployed using the pipeline. I know it's possible to save the models to the Dev workspace files, then copy them to the test / prod workspace files, then re-register the models from the files in to the model registry. However this process contains a lot of manual steps which introduces more room for error.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I was hoping that someone could point to a successful use case that I can reference or give advice on the best way to structure the workspaces for ML Ops.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;</description>
    <pubDate>Tue, 01 Jul 2025 19:28:54 GMT</pubDate>
    <dc:creator>DCELL</dc:creator>
    <dc:date>2025-07-01T19:28:54Z</dc:date>
    <item>
      <title>Workspace advice for ML tasks</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4750127#M799</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I need some advice/example use case for using ML in Fabric.&lt;/P&gt;&lt;P&gt;We have a Dev workspace containing artifacts for ingesting data, storing data, cleaning &amp;amp; feature engineering, model training &amp;amp; logging experiments, and finally the best model is selected from the experiment and saved as a new version of the model.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;The problem is ML models are the only artifacts that can't be directly deployed using the pipeline. I know it's possible to save the models to the Dev workspace files, then copy them to the test / prod workspace files, then re-register the models from the files in to the model registry. However this process contains a lot of manual steps which introduces more room for error.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I was hoping that someone could point to a successful use case that I can reference or give advice on the best way to structure the workspaces for ML Ops.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;</description>
      <pubDate>Tue, 01 Jul 2025 19:28:54 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4750127#M799</guid>
      <dc:creator>DCELL</dc:creator>
      <dc:date>2025-07-01T19:28:54Z</dc:date>
    </item>
    <item>
      <title>Re: Workspace advice for ML tasks</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4750786#M801</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="javascript:void(0)" data-lia-user-mentions="" data-lia-user-uid="244291" data-lia-user-login="DCELL" class="lia-mention lia-mention-user"&gt;DCELL&lt;/a&gt;&amp;nbsp;,&lt;BR /&gt;&lt;SPAN&gt;Thanks for reaching out to the Microsoft fabric community forum.&amp;nbsp;&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;/P&gt;
&lt;P&gt;Yes,you're absolutely right that Fabric currently doesn’t support deploying ML models via deployment pipelines, and that adds complexity when aligning with proper MLOps practices.&lt;/P&gt;
&lt;P&gt;&lt;A title="https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-first-experiment-automated-ml?view=azureml-api-2" href="https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-first-experiment-automated-ml?view=azureml-api-2" target="_blank" rel="noreferrer noopener"&gt;Tutorial: AutoML- train no-code classification models - Azure Machine Learning | Microsoft Learn&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&lt;A title="https://learn.microsoft.com/en-us/fabric/data-science/automated-ml-fabric" href="https://learn.microsoft.com/en-us/fabric/data-science/automated-ml-fabric" target="_blank" rel="noreferrer noopener"&gt;Automated ML in Fabric - Microsoft Fabric | Microsoft Learn&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;One of the user raised it in the Issues forum please go through the link:&lt;/P&gt;
&lt;P&gt;&lt;A title="https://community.fabric.microsoft.com/t5/Issues/Deployment-Pipelines-unsupported-items-ML-Model/idi-p/4647290" href="https://community.fabric.microsoft.com/t5/Issues/Deployment-Pipelines-unsupported-items-ML-Model/idi-p/4647290" target="_blank" rel="noreferrer noopener"&gt;Deployment Pipelines unsupported items - ML Model - Microsoft Fabric Community&lt;/A&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Please go through the below solved link which may help you in resolving the issue:&lt;/P&gt;
&lt;P&gt;&lt;A title="https://community.fabric.microsoft.com/t5/Data-Science/Machine-learning-pipelines-in-Microsoft-Fabric/m-p/3810695" href="https://community.fabric.microsoft.com/t5/Data-Science/Machine-learning-pipelines-in-Microsoft-Fabric/m-p/3810695" target="_blank" rel="noreferrer noopener"&gt;Solved: Machine learning pipelines in Microsoft Fabric - Microsoft Fabric Community&lt;/A&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN data-teams="true"&gt;I hope this information helps. Please do let us know if you have any further queries.&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Wed, 02 Jul 2025 10:06:45 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4750786#M801</guid>
      <dc:creator>v-menakakota</dc:creator>
      <dc:date>2025-07-02T10:06:45Z</dc:date>
    </item>
    <item>
      <title>Re: Workspace advice for ML tasks</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4754412#M804</link>
      <description>&lt;P&gt;&lt;SPAN data-contrast="auto"&gt;Hi&lt;/SPAN&gt;&lt;SPAN data-ccp-props="{}"&gt;&amp;nbsp;&amp;nbsp;&lt;a href="javascript:void(0)" data-lia-user-mentions="" data-lia-user-uid="244291" data-lia-user-login="DCELL" class="lia-mention lia-mention-user"&gt;DCELL&lt;/a&gt;&amp;nbsp;,&lt;BR /&gt;&lt;BR /&gt;&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN data-contrast="auto"&gt;May I ask if you have resolved this issue? If you have any issues please reach out to us.&lt;BR /&gt;Thank you.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 07 Jul 2025 04:31:50 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4754412#M804</guid>
      <dc:creator>v-menakakota</dc:creator>
      <dc:date>2025-07-07T04:31:50Z</dc:date>
    </item>
    <item>
      <title>Re: Workspace advice for ML tasks</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4755416#M805</link>
      <description>&lt;P&gt;I'll mark it as resolved. I guess we just have to wait for a Fabric update to address this.&lt;/P&gt;</description>
      <pubDate>Mon, 07 Jul 2025 13:30:52 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Science/Workspace-advice-for-ML-tasks/m-p/4755416#M805</guid>
      <dc:creator>DCELL</dc:creator>
      <dc:date>2025-07-07T13:30:52Z</dc:date>
    </item>
  </channel>
</rss>

