DOCS-1472: Use case page train and deploy a classification model (#2258)

docs/use-cases/deploy-ml.md

@@ -3,6 +3,63 @@ title: "Train and deploy image classification models"
 linkTitle: "Train and deploy classification models"
 weight: 50
 type: "docs"
-layout: "empty"
-canonical: "ml/"
+tags: ["data management", "data", "services"]
+no_list: true
+description: "Use Viam's machine learning capabilities to train image classification models and deploy these models to your machines."
+image: "/ml/training.png"
+imageAlt: "Machine Learning"
+images: ["/ml/training.png"]
 ---
+
+You can create and deploy an image classification model onto your machine with Viam's machine learning (ML) capabilities.
+Manage the classification model fully on one platform: collect data, create a dataset and label it, and train the model for **Single** or **Multi Label Classification**.
+Then, test if your model works for classifying objects in a camera stream or existing images with the `mlmodel` classification model of vision service.
+
+<table>
+  <tr>
+    <th>{{<imgproc src="/ml/collect.svg" class="fill alignright" style="max-width: 300px" declaredimensions=true alt="Collect data">}}
+      <b>1. Collect</b>
+      <p>Start by collecting images from your cameras with the <a href="/data/">data management service</a>. You can <a href="/data/view/">view the data</a> on the <b>Data tab</b>.</p>
+    </th>
+  </tr>
+  <tr>
+    <th>{{<imgproc src="/ml/label.svg" class="fill alignleft" style="max-width: 300px" declaredimensions=true alt="Label data">}}
+      <b>2. Create a dataset and label</b>
+      <p>Once you have enough images of the objects you'd like to classify, <a href="/data/dataset/">label your data and create a dataset</a> in preparation for training classification models.</p>
+    </th>
+  </tr>
+  <tr>
+    <th>{{<imgproc src="/ml/train.svg" class="fill alignright" style="max-width: 300px" declaredimensions=true alt="Train models">}}
+      <b>3. Train an ML model</b>
+      <p>Use your labeled data to <a href="/ml/train-model/">train your own models</a> for object classification using data from the <a href="/data/">data management service</a>.</p>
+    </th>
+  </tr>
+  <tr>
+    <td>
+      <b>4. Deploy your ML model</b>
+      <p>To make use of ML models with your machine, use the built-in <a href="/ml/">ML model service</a> to deploy and run the model.</p>
+    </td>
+  </tr>
+  <tr>
+    <td>{{<imgproc src="/ml/configure.svg" class="fill alignleft" style="max-width: 300px" declaredimensions=true alt="Configure a service">}}
+      <b>5. Configure an <code>mlmodel</code> vision service</b>
+      <p>For object classification, you can use the <a href="/ml/vision/">vision service</a>, which provides an <a href="/ml/vision/classification/#configure-an-mlmodel-classifier">ml model classifier</a> model.</p>
+</td>
+  </tr>
+  <tr>
+    <td>{{<imgproc src="ml/deploy.svg" class="fill alignright" style="max-width: 300px" declaredimensions=true alt="Deploy your model">}}
+      <b>6. Test your classifier</b>
+      <p>Test your <a href="/ml/vision/classification/#test-your-classifier">mlmodel classifier</a> with <a href="/ml/vision/classification/#existing-images-in-the-cloud">existing images in the Viam app</a>, <a href="/ml/vision/classification/#live-camera-footage">live camera footage,</a> or <a href="/ml/vision/classification/#existing-images-on-your-machine">existing images on a computer</a>.</p>
+    </td>
+  </tr>
+</table>
+
+## Next steps
+
+After testing your classifier, see the following to further explore Viam's data management and computer vision capabilities:
+
+- [Export Data Using the Viam CLI](/data/export/): Export your synced data from the Viam cloud.
+- [2D Object Detection](/ml/vision/detection/): Configure your machine's camera to draw a bounding box around detected objects, based on a machine learning model.
+- [Update an existing ML model](/ml/train-model/#train-a-new-version-of-a-model): Refine an existing ML model you have trained, and select which model version to deploy.
+
+You can also explore our [tutorials](/tutorials/) for more ideas.