docs(mif): added guideflow and updated some pages (#5302)

Author: jcirinosclwy

pages/managed-inference/how-to/create-deployment.mdx

@@ -1,9 +1,9 @@
 ---
 title: How to deploy a model on Scaleway Managed Inference
-description: This page explains how to deploy a model on Scaleway Managed Inference
-tags: managed-inference ai-data creating dedicated
+description: This page explains how to deploy a Managed Inference model on the Scaleway console.
+tags: managed-inference ai-data
 dates:
-  validation: 2025-04-09
+  validation: 2025-07-21
   posted: 2024-03-06
 ---
 import Requirements from '@macros/iam/requirements.mdx'
@@ -23,24 +23,24 @@ import Requirements from '@macros/iam/requirements.mdx'
           Scaleway Managed Inference allows you to deploy various AI models, either from the Scaleway catalog or by importing a custom model. For detailed information about supported models, visit our [Supported models in Managed Inference](/managed-inference/reference-content/supported-models/) documentation.
         </Message>
         <Message type="note">
-          Some models may require acceptance of an end-user license agreement. If prompted, review the terms and conditions and accept the license accordingly.
+          Some models may require acceptance of an end-user license agreement (EULA). If prompted, review the terms and conditions and accept the license accordingly.
         </Message>
     - Choose the geographical **region** for the deployment.
     - For custom models: Choose the model quantization.
       <Message type="tip">
         Each model comes with a default quantization. Select lower bits quantization to improve performance and enable the model to run on smaller GPU nodes, while potentially reducing precision.
       </Message>
-    - Specify the GPU Instance type to be used with your deployment.
-5. Choose the number of nodes for your deployment. Note that this feature is currently in [Public Beta](https://www.scaleway.com/betas/).
-    <Message type="note">
-      High availability is only guaranteed with two or more nodes.
-    </Message>
-6. Enter a **name** for the deployment, and optional tags.
-7. Configure the **network connectivity** settings for the deployment:
+    - Select a node type, the GPU Instance that will be used with your deployment.
+    - Choose the number of nodes for your deployment. Note that this feature is currently in [Public Beta](https://www.scaleway.com/betas/).
+      <Message type="tip">
+        High availability is only guaranteed with two or more nodes.
+      </Message>
+5. Enter a **name** for the deployment, and optional tags.
+6. Configure the **network connectivity** settings for the deployment:
     - Attach to a **Private Network** for secure communication and restricted availability. Choose an existing Private Network from the drop-down list, or create a new one.
     - Set up **Public connectivity** to access resources via the public internet. Authentication by API key is enabled by default.
     <Message type="important">
       - Enabling both private and public connectivity will result in two distinct endpoints (public and private) for your deployment.
       - Deployments must have at least one endpoint, either public or private.
     </Message>
-8. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.
+7. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.

pages/managed-inference/how-to/delete-deployment.mdx

@@ -1,17 +1,17 @@
 ---
 title: How to delete a Managed Inference deployment
-description: This page explains how to delete a Managed Inference deployment
-tags: managed-inference ai-data deleting
+description: This page explains how to delete a Managed Inference deployment via the Scaleway console.
+tags: managed-inference ai-data delete
 dates:
-  validation: 2025-03-19
+  validation: 2025-07-21
   posted: 2024-03-06
 categories:
   - ai-data
 ---
 import Requirements from '@macros/iam/requirements.mdx'
 
 
-Once you have finished your inference tasks you can delete your deployment. This page explains how to do so from the Scaleway console.
+Once you have finished your inference tasks, you can delete your deployment. This page explains how to do so from the Scaleway console.
 
 <Requirements />
 
@@ -21,11 +21,13 @@ Once you have finished your inference tasks you can delete your deployment. This
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
 2. From the drop-down menu, select the geographical region you want to manage.
-3. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
-4. Click the **Settings** tab of your deployment to display additional settings.
-5. Click **Delete deployment**.
-6. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
+3. Choose a deployment by clicking its name. The deployment's **Overview** page displays.
+4. Navigate to the **Settings** tab.
+5. Click **Delete deployment** at the bottom of the page.
+6. Type **DELETE** to confirm and click **Delete deployment**.
+
+  Alternatively, from the Deployments listing, click the <Icon name="more" /> icon next to the deployment name you no longer need, and click **Delete**. A pop-up appears. Type **DELETE** to confirm, then click **Delete deployment**.
 
 <Message type="important">
-  Deleting a deployment is a permanent action and will erase all its associated data.
+  Deleting a deployment is a permanent action that erases all its associated data and resources.
 </Message>

pages/managed-inference/quickstart.mdx

@@ -3,7 +3,7 @@ title: Managed Inference - Quickstart
 description: Start with Scaleway Managed Inference for secure, scalable AI model deployment in Europe's premier platform. Privacy-focused, fully managed.
 tags:
 dates:
-  validation: 2025-02-24
+  validation: 2025-07-21
 categories:
   - ai-data
 ---
@@ -22,6 +22,11 @@ Here are some of the key features of Scaleway Managed Inference:
 * **Complete data privacy**: [No storage](/managed-inference/reference-content/data-privacy-security-scaleway-ai-services/#data-storage-policies) or third-party access to your data (prompt or responses), to ensure it remains exclusively yours.
 * **Interoperability**: Scaleway Managed Inference was designed as a drop-in [replacement for the OpenAI APIs](/managed-inference/reference-content/openai-compatibility/), for a seamless transition on your applications already using its libraries.
 
+## Console overview
+
+Discover the Managed Inference interface on the Scaleway console.
+<GuideFlow src="https://app.guideflow.com/embed/0p0ozjebvp"/>
+
 <Requirements />
 
   - A Scaleway account logged into the [console](https://console.scaleway.com)
@@ -38,11 +43,14 @@ Here are some of the key features of Scaleway Managed Inference:
           Scaleway Managed Inference allows you to deploy various AI models, either from the Scaleway catalog or by importing a custom model. For detailed information about supported models, visit our [Supported models in Managed Inference](/managed-inference/reference-content/supported-models/) documentation.
         </Message>
         <Message type="note">
-          Some models may require acceptance of an end-user license agreement. If prompted, review the terms and conditions and accept the license accordingly.
+          Some models may require acceptance of an end-user license agreement (EULA). If prompted, review the terms and conditions and accept the license accordingly.
         </Message>
     - Choose the geographical **region** for the deployment.
-    - Specify the GPU Instance type to be used with your deployment.
+    - Select a node type, the GPU Instance that will be used with your deployment.
     - Choose the number of nodes for your deployment. Note that this feature is currently in [Public Beta](https://www.scaleway.com/betas/).
+        <Message type="note">
+          High availability is only guaranteed with two or more nodes.
+        </Message>
 5. Enter a **name** for the deployment, along with optional tags to aid in organization.
 6. Configure the **network** settings for the deployment:
     - Enable **Private Network** for secure communication and restricted availability within Private Networks. Choose an existing Private Network from the drop-down list, or create a new one.
@@ -59,9 +67,10 @@ Managed Inference deployments have authentication enabled by default. As such, y
 
 1. Click **Managed Inference** in the **AI** section of the side menu. The Managed Inference dashboard displays.
 2. From the drop-down menu, select the geographical region where you want to manage.
-3. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
-4. Click **Generate key** in the **Deployment connection** section of the dashboard. The token creation wizard displays.
+3. Click the name of the deployment you wish to access. The deployment's **Overview** page displays.
+4. Scroll down to the **Deployment authentication** section and click the **Generate key** button. The token creation wizard displays.
 5. Fill in the [required information for API key creation](/iam/how-to/create-api-keys/) and click **Generate API key**.
+6. Copy and safely store your credentials before closing the window, as they will not be shown again.
 
 <Message type="tip">
   You have full control over authentication from the **Security** tab of your deployment. Authentication is enabled by default.
@@ -70,9 +79,9 @@ Managed Inference deployments have authentication enabled by default. As such, y
 ## How to interact with Managed Inference
 
 1. Click **Managed Inference** in the **AI** section of the side menu. The Managed Inference dashboard displays.
-2. From the drop-down menu, select the geographical region where you want to manage.
-3. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
-4. Click the **Inference** tab. Code examples in various environments display. Copy and paste them into your code editor or terminal.
+2. From the drop-down menu, select the geographical region where your desired deployment was created.
+3. Click the name of the deployment you wish to edit. The deployment's **Overview** page displays.
+4. Click the **Playground** tab, then **View code** to see code examples in various environments. Copy and paste them into your code editor or terminal.
 
 <Message type="note">
   Prompt structure may vary from one model to another. Refer to the specific instructions for use in our [dedicated documentation](/managed-inference/reference-content/).
@@ -81,12 +90,14 @@ Managed Inference deployments have authentication enabled by default. As such, y
 ## How to delete a deployment
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. From the drop-down menu, select the geographical region where you want to create your deployment.
-3. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
-4. Click the **Settings** tab of your deployment to display additional settings.
-5. Click **Delete deployment**.
-6. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
+2. From the drop-down menu, select the geographical region where your deployment was created.
+3. Click the name of the deployment you wish to delete.
+4. Navigate to the **Settings** tab.
+5. Click **Delete deployment** at the bottom of the page.
+6. Type **DELETE** to confirm and click **Delete deployment**.
+
+  Alternatively, from the Deployments listing, click the <Icon name="more" /> icon next to the deployment name you no longer need, and click **Delete**. A pop-up appears. Type **DELETE** to confirm, then click **Delete deployment**.
 
 <Message type="important">
-  Deleting a deployment is a permanent action, and will erase all its associated configuration and resources.
+  Deleting a deployment is a permanent action that erases all its associated data and resources.
 </Message>