Merge pull request #9079 from microsoft/ntrogh/sync-ignite-updates

Merge Ignite updates in repo

Author: ntrogh

blogs/2025/11/18/PrivateMarketplace.md

@@ -0,0 +1,45 @@
+---
+Order: 120
+TOCTitle: "Announcing Private Marketplace for VS Code"
+PageTitle: "Announcing Private Marketplace for VS Code"
+MetaDescription: Private Marketplace for VS Code extensions now generally available.
+MetaSocialImage: PrivateMarketplaceHero.png
+Date: 2025-11-18
+Author: Sean Iyer
+---
+
+# Introducing the Visual Studio Code Private Marketplace: Your Team's Secure, Curated Extension Hub 🎉
+
+November 18, 2025 by [Sean Iyer](https://x.com/nuget)
+
+Developers shouldn't have to juggle manual installs, worry about unverified extensions, or chase compliance exceptions just to get their favorite tools. Today, we're excited to announce Private Marketplace for VS Code—a dedicated, enterprise-ready hub that puts you in full control of how extensions are sourced, reviewed, and distributed to your dev teams. 🔐✨
+
+<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/nQLdmy50cb0?si=URhzNdCQ4a4zOSBx" title="Video showing the VS Code private marketplace." frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+## With the Private Marketplace, you can:
+
+- Curate exactly which extensions appear for your organization, whether they're internal tools or trusted public plugins.
+- Lock down sensitive IP by privately hosting your own extensions—no more sharing proprietary code outside your firewall.
+- Easily rehost vetted public extensions after your own checks, so your teams can use familiar tools in secure or air-gapped environments.
+- Roll out everything centrally—new users get a consistent, up-to-date catalog from day one.
+
+## Same great VS Code experience
+
+Fully integrated into the VS Code experience, your developers will find, install, and update extensions as easily as ever—without leaving the safety of your managed ecosystem. No more ticket-based installs, zip file confusion, or security compromises.
+
+![VS Code Private Marketplace](./vscpmga.png)
+
+> [!NOTE]
+> Private Marketplace is available to GitHub Enterprise customers. VS Code users must sign in with a GitHub Enterprise or Copilot Business or Enterprise account to access.
+
+## Getting started
+
+Ready to streamline, secure, and scale your extension management? Start with our deployment guide now and bring peace of mind to both your developers and your IT team.
+
+👉 See [deployment and feature guide](https://aka.ms/private-marketplace/readme) for instructions, scripts, and environment setup.
+
+💬 Need help? Contact [private marketplace support](https://aka.ms/vspm/support).
+
+### 🎯 Start today and empower your teams with a secure, streamlined extension experience!
+
+Happy coding!

blogs/2025/11/18/PrivateMarketplaceHero.png

@@ -364,6 +364,10 @@ As of VS Code release 1.97, when you first install an extension from a third-par
 
 Get more information about [extension runtime security](/docs/configure/extensions/extension-runtime-security.md).
 
+### Can I host extensions internally for my organization?
+
+Yes, see the [Private Marketplace for Extensions](https://code.visualstudio.com/docs/setup/enterprise#_private-marketplace-for-extensions).
+
 ### The extension signature cannot be verified by VS Code
 
 The Visual Studio Marketplace signs all extensions when they are published. VS Code verifies this signature when you install an extension to check the integrity and the source of the extension package.
@@ -372,9 +376,10 @@ The Visual Studio Marketplace signs all extensions when they are published. VS C
 > When you install an extension, you might see the following error message: `Cannot install extension because Visual Studio Code cannot verify the extension signature`. This error can be caused by a variety of reasons and should you encounter this error, exercise caution before deciding to install anyway. Disable extension signature verification with the `setting(extensions.verifySignature)` setting.
 
 #### Package integrity issues
+
 For package integrity issues, it's recommended that you contact the [Visual Studio Marketplace team](mailto:[email protected]?subject=Extension%20Signature%20Verification%20Issue) to report the issue. Make sure to include the extension ID. The following list provides error codes related to package integrity issues:
 
-```
+```text
 PackageIntegrityCheckFailed
 SignatureIsInvalid
 SignatureManifestIsInvalid
@@ -389,6 +394,7 @@ NotSigned
 ```
 
 #### Other issues
+
 For other issues like an unsupported environment or unknown reasons, it's recommended that you [report an issue](https://github.com/microsoft/vscode/issues/new) with VS Code by providing all necessary information and including the shared log: `kb(workbench.action.showCommands)` > **Open View...** > **Shared**.
 
 ### My extensions don't synchronize when connected to a remote window

blogs/2025/11/18/vscpmga.png

@@ -7,7 +7,7 @@ MetaSocialImage: images/datascience/fabric-social.png
 
 # Data science in Microsoft Fabric using Visual Studio Code
 
-You can build and develop data science and data engineering solutions for [Microsoft Fabric](https://learn.microsoft.com/fabric/) within VS Code. [Microsoft Fabric](https://marketplace.visualstudio.com/items?itemName=fabric.vscode-fabric) extensions for VS Code provide an integrated development experience for working with Fabric artifacts, lakehouses, notebooks, and user data functions. 
+You can build and develop data science and data engineering solutions for [Microsoft Fabric](https://learn.microsoft.com/fabric/) within VS Code. [Microsoft Fabric](https://marketplace.visualstudio.com/items?itemName=fabric.vscode-fabric) extensions for VS Code provide an integrated development experience for working with Fabric artifacts, lakehouses, notebooks, and user data functions.
 
 ## What is Microsoft Fabric?
 
@@ -36,23 +36,26 @@ You can find and install the extensions from the [Visual Studio Marketplace](htt
 | **Fabric Data Engineering** | Data engineers working with large-scale data & Spark | - Explore Lakehouses (tables, raw files)<br>- Develop/debug Spark notebooks<br>- Build/test Spark job definitions<br>- Sync notebooks between local VS Code & Fabric<br>- Preview schemas & sample data | You work with Spark, Lakehouses, or large-scale data pipelines and want to explore, develop, and debug locally. | [Develop Fabric notebooks in VS Code](https://learn.microsoft.com/fabric/data-engineering/setup-vs-code-extension) |
 
 ## Getting started
+
 Once you have the extensions installed and signed in, you can start working with Fabric workspaces and items. In the Command Palette (`kb(workbench.action.showCommands)`), type **Fabric** to list the commands that are specific to Microsoft Fabric.
+
 ![Diagram that shows all microsoft Fabric commands](images/microsoft-fabric/fabric-command-palette.png)
 
 ## Fabric Workspace and items explorer
 
 The Fabric extensions provide a seamless way to work with both remote and local Fabric items.
-- In the Fabric extension, the **Fabric Workspaces** section lists all items from your remote workspace, organized by type (Lakehouses, Notebooks, Pipelines, and more).
-- In the Fabric extension, the **Local folder** section shows a Fabric item(s) folder opened in VS Code. It reflects the structure of your fabric item definition for each type that is opened in VS Code. This enables you to develop locally and publish your changes to current or new workspace.
+
+* In the Fabric extension, the **Fabric Workspaces** section lists all items from your remote workspace, organized by type (Lakehouses, Notebooks, Pipelines, and more).
+* In the Fabric extension, the **Local folder** section shows a Fabric item(s) folder opened in VS Code. It reflects the structure of your fabric item definition for each type that is opened in VS Code. This enables you to develop locally and publish your changes to current or new workspace.
 
 ![Screenshot that shows how to view your workspaces and items?](images/microsoft-fabric/view-workspaces-and-items.png)
 
 ## Use user data functions for data science
 
 1. In the Command Palette (`kb(workbench.action.showCommands)`), type **Fabric: Create Item**.
-2. Select your workspace and select **User data function**. Provide a name and select **Python** language.
-3. You are notified to set up the Python virtual environment and continue to set this up locally.
-4. Install the libraries using `pip install` or select the user data function item in the Fabric extension to add libraries. Update the `requirements.txt` file to specify the dependencies:
+1. Select your workspace and select **User data function**. Provide a name and select **Python** language.
+1. You are notified to set up the Python virtual environment and continue to set this up locally.
+1. Install the libraries using `pip install` or select the user data function item in the Fabric extension to add libraries. Update the `requirements.txt` file to specify the dependencies:
 
     ```txt
     fabric-user-data-functions ~= 1.0
@@ -63,108 +66,111 @@ The Fabric extensions provide a seamless way to work with both remote and local
     joblib=1.2.0
     ```
 
-4. Open `functions_app.py`. Here's an example of developing a User Data Function for data science using scikit-learn:
+1. Open `functions_app.py`. Here's an example of developing a User Data Function for data science using scikit-learn:
 
-```python
-import datetime
-import fabric.functions as fn
-import logging
-
-# Import additional libraries
-import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
-import joblib
-
-udf = fn.UserDataFunctions()
-@udf.function()
-def train_churn_model(data: list, targetColumn: str) -> dict:
-    '''
-    Description: Train a Random Forest model to predict customer churn using pandas and scikit-learn.
-
-    Args:
-    - data (list): List of dictionaries containing customer features and churn target
-      Example: [{"Age": 25, "Income": 50000, "Churn": 0}, {"Age": 45, "Income": 75000, "Churn": 1}]
-    - targetColumn (str): Name of the target column for churn prediction
-      Example: "Churn"
-
-    Returns: dict: Model training results including accuracy and feature information
-    '''
-    # Convert data to DataFrame
-    df = pd.DataFrame(data)
-
-    # Prepare features and target
-    numeric_features = df.select_dtypes(include=['number']).columns.tolist()
-    numeric_features.remove(targetColumn)
-
-    X = df[numeric_features]
-    y = df[targetColumn]
-
-    # Split and scale data
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    scaler = StandardScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-    X_test_scaled = scaler.transform(X_test)
-
-    # Train model
-    model = RandomForestClassifier(n_estimators=100, random_state=42)
-    model.fit(X_train_scaled, y_train)
-
-    # Evaluate and save
-    accuracy = accuracy_score(y_test, model.predict(X_test_scaled))
-    joblib.dump(model, 'churn_model.pkl')
-    joblib.dump(scaler, 'scaler.pkl')
-
-    return {
-        'accuracy': float(accuracy),
-        'features': numeric_features,
-        'message': f'Model trained with {len(X_train)} samples and {accuracy:.2%} accuracy'
-    }
-
-@udf.function()
-def predict_churn(customer_data: list) -> list:
-    '''
-    Description: Predict customer churn using trained Random Forest model.
-
-    Args:
-    - customer_data (list): List of dictionaries containing customer features for prediction
-      Example: [{"Age": 30, "Income": 60000}, {"Age": 55, "Income": 80000}]
-
-    Returns: list: Customer data with churn predictions and probability scores
-    '''
-    # Load saved model and scaler
-    model = joblib.load('churn_model.pkl')
-    scaler = joblib.load('scaler.pkl')
-
-    # Convert to DataFrame and scale features
-    df = pd.DataFrame(customer_data)
-    X_scaled = scaler.transform(df)
-
-    # Make predictions
-    predictions = model.predict(X_scaled)
-    probabilities = model.predict_proba(X_scaled)[:, 1]
-
-    # Add predictions to original data
-    results = customer_data.copy()
-    for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
-        results[i]['churn_prediction'] = int(pred)
-        results[i]['churn_probability'] = float(prob)
-
-    return results
-```
+    ```python
+    import datetime
+    import fabric.functions as fn
+    import logging
+
+    # Import additional libraries
+    import pandas as pd
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score
+    import joblib
+
+    udf = fn.UserDataFunctions()
+    @udf.function()
+    def train_churn_model(data: list, targetColumn: str) -> dict:
+        '''
+        Description: Train a Random Forest model to predict customer churn using pandas and scikit-learn.
+
+        Args:
+        - data (list): List of dictionaries containing customer features and churn target
+        Example: [{"Age": 25, "Income": 50000, "Churn": 0}, {"Age": 45, "Income": 75000, "Churn": 1}]
+        - targetColumn (str): Name of the target column for churn prediction
+        Example: "Churn"
+
+        Returns: dict: Model training results including accuracy and feature information
+        '''
+        # Convert data to DataFrame
+        df = pd.DataFrame(data)
+
+        # Prepare features and target
+        numeric_features = df.select_dtypes(include=['number']).columns.tolist()
+        numeric_features.remove(targetColumn)
+
+        X = df[numeric_features]
+        y = df[targetColumn]
+
+        # Split and scale data
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        scaler = StandardScaler()
+        X_train_scaled = scaler.fit_transform(X_train)
+        X_test_scaled = scaler.transform(X_test)
+
+        # Train model
+        model = RandomForestClassifier(n_estimators=100, random_state=42)
+        model.fit(X_train_scaled, y_train)
+
+        # Evaluate and save
+        accuracy = accuracy_score(y_test, model.predict(X_test_scaled))
+        joblib.dump(model, 'churn_model.pkl')
+        joblib.dump(scaler, 'scaler.pkl')
+
+        return {
+            'accuracy': float(accuracy),
+            'features': numeric_features,
+            'message': f'Model trained with {len(X_train)} samples and {accuracy:.2%} accuracy'
+        }
+
+    @udf.function()
+    def predict_churn(customer_data: list) -> list:
+        '''
+        Description: Predict customer churn using trained Random Forest model.
+
+        Args:
+        - customer_data (list): List of dictionaries containing customer features for prediction
+        Example: [{"Age": 30, "Income": 60000}, {"Age": 55, "Income": 80000}]
+
+        Returns: list: Customer data with churn predictions and probability scores
+        '''
+        # Load saved model and scaler
+        model = joblib.load('churn_model.pkl')
+        scaler = joblib.load('scaler.pkl')
+
+        # Convert to DataFrame and scale features
+        df = pd.DataFrame(customer_data)
+        X_scaled = scaler.transform(df)
+
+        # Make predictions
+        predictions = model.predict(X_scaled)
+        probabilities = model.predict_proba(X_scaled)[:, 1]
+
+        # Add predictions to original data
+        results = customer_data.copy()
+        for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
+            results[i]['churn_prediction'] = int(pred)
+            results[i]['churn_probability'] = float(prob)
+
+        return results
+    ```
+
+1. Test your functions locally, by pressing `kbstyle(F5)`.
+1. In the Fabric extension, in **Local folder** , select the function and publish to your workspace.
 
-6. Test your functions locally, by pressing `kbstyle(F5)`.
-7. In the Fabric extension, in **Local folder** , select the function and publish to your workspace.
     ![Screenshot that shows how to publish your user data funtions item](./images/microsoft-fabric/publish-user-data-function.png)
 
 Learn more about invoking the function from:
-- [Fabric Data pipelines](https://learn.microsoft.com/fabric/data-engineering/user-data-functions/create-functions-activity-data-pipelines)
-- [Fabric Notebooks](https://learn.microsoft.com/fabric/data-engineering/notebook-utilities#user-data-function-udf-utilities)
--  [An external application](https://learn.microsoft.com/fabric/data-engineering/user-data-functions/tutorial-invoke-from-python-app)
+
+* [Fabric Data pipelines](https://learn.microsoft.com/fabric/data-engineering/user-data-functions/create-functions-activity-data-pipelines)
+* [Fabric Notebooks](https://learn.microsoft.com/fabric/data-engineering/notebook-utilities#user-data-function-udf-utilities)
+* [An external application](https://learn.microsoft.com/fabric/data-engineering/user-data-functions/tutorial-invoke-from-python-app)
 
 ## Use Fabric notebooks for data science
+
 A Fabric notebook is an interactive workbook in Microsoft Fabric for writing and running code, visualizations, and markdown side-by-side. Notebooks support multiple languages (Python, Spark, SQL, Scala, and more) and are ideal for data exploration, transformation, and model development in Fabric working with your existing data in OneLake.
 
 ### Example
@@ -199,6 +205,7 @@ def train_logistic_from_spark(spark, csv_path):
 Refer to [Microsoft Fabric Notebooks](https://learn.microsoft.com/fabric/data-engineering/how-to-use-notebook) documentation to learn more.
 
 ## Git integration
+
 Microsoft Fabric supports Git integration that enables version control and collaboration across data and analytics projects. You can connect a Fabric workspace to Git repositories, primarily Azure DevOps or GitHub, and only supported items are synced. This integration also supports CI/CD workflow to enable teams to manage releases efficiently and maintain high-quality analytics environments.
 
 ![GIF that shows how to use Git integration with User data functions](./images/microsoft-fabric/fabric-git-integration.gif)
@@ -207,12 +214,11 @@ Microsoft Fabric supports Git integration that enables version control and colla
 
 Now that you have Microsoft Fabric extensions set up in VS Code, explore these resources to deepen your knowledge:
 
-### Learn more about Microsoft Fabric
 * [Learn about Microsoft Fabric for Data Science](https://learn.microsoft.com/en-us/fabric/data-science/tutorial-data-science-introduction).
 * [Set up your Fabric trial capacity](https://learn.microsoft.com/fabric/fundamentals/fabric-trial)
 * [Microsoft Fabric fundamentals](https://learn.microsoft.com/fabric/fundamentals/fabric-overview)
 
-### Community and support
+To engage with the community and get support:
 
 * [Microsoft Fabric community forums](https://community.fabric.microsoft.com/)
 * [Fabric samples and templates](https://github.com/microsoft/fabric-samples)