DOCS-4086: code samples for building a good dataset

.github/workflows/update_sdk_methods.py

@@ -342,7 +342,7 @@
     "frame": "/services/frame-system/",
     "Viam app": "https://app.viam.com/",
     "organization settings page": "/manage/reference/organize/",
-    "image tags": "/data-ai/ai/create-dataset/#label-your-images",
+    "image tags": "/data-ai/train/create-dataset/#label-your-images",
     "API key": "/fleet/cli/#authenticate",
     "board model": "/dev/reference/apis/components/board/"
 }

docs/data-ai/_index.md

@@ -25,11 +25,12 @@ You can also monitor your machines through teleop, power your application logic,
 
 <div class="hoveraction">
 
-{{< how-to-expand "Capture data" "3" "BEGINNER-FRIENDLY" "" "data-platform-capture" >}}
+{{< how-to-expand "Capture data" "4" "BEGINNER-FRIENDLY" "" "data-platform-capture" >}}
 {{< cards >}}
 {{% card link="/data-ai/capture-data/capture-sync/" noimage="true" %}}
 {{% card link="/data-ai/capture-data/filter-before-sync/" noimage="true" %}}
 {{% card link="/data-ai/capture-data/conditional-sync/" noimage="true" %}}
+{{% card link="/data-ai/capture-data/lorawan/" noimage="true" %}}
 {{< /cards >}}
 {{< /how-to-expand >}}
 
@@ -42,11 +43,18 @@ You can also monitor your machines through teleop, power your application logic,
 {{< /cards >}}
 {{< /how-to-expand >}}
 
-{{< how-to-expand "Leverage AI" "8" "INTERMEDIATE" "" "data-platform-ai" >}}
+{{< how-to-expand "Train an ML model" "5" "INTERMEDIATE" "" "data-platform-ai" >}}
+{{< cards >}}
+{{% card link="/data-ai/train/create-dataset/" noimage="true" %}}
+{{% card link="/data-ai/train/capture-annotate-images/" noimage="true" %}}
+{{% card link="/data-ai/train/train-tflite/" noimage="true" %}}
+{{% card link="/data-ai/train/train/" noimage="true" %}}
+{{% card link="/data-ai/train/upload-external-data/" noimage="true" %}}
+{{< /cards >}}
+{{< /how-to-expand >}}
+
+{{< how-to-expand "Infer with ML models" "4" "INTERMEDIATE" "" "data-platform-ai" >}}
 {{< cards >}}
-{{% card link="/data-ai/ai/create-dataset/" noimage="true" %}}
-{{% card link="/data-ai/ai/train-tflite/" noimage="true" %}}
-{{% card link="/data-ai/ai/train/" noimage="true" %}}
 {{% card link="/data-ai/ai/deploy/" noimage="true" %}}
 {{% card link="/data-ai/ai/run-inference/" noimage="true" %}}
 {{% card link="/data-ai/ai/alert/" noimage="true" %}}

docs/data-ai/ai/_index.md

@@ -1,6 +1,6 @@
 ---
-linkTitle: "Leverage AI"
-title: "Leverage AI"
+linkTitle: "Infer with ML models"
+title: "Infer with ML models"
 weight: 300
 layout: "empty"
 type: "docs"

docs/data-ai/ai/act.md

@@ -5,7 +5,7 @@ weight: 70
 layout: "docs"
 type: "docs"
 description: "Use the vision service API to act based on inferences."
-next: "/data-ai/ai/advanced/upload-external-data/"
+next: "/data-ai/train/upload-external-data/"
 ---
 
 You can use the [vision service API](/dev/reference/apis/services/vision/) to get information about your machine's inferences and program behavior based on that.