DOCS-3423: Add some feedback from Adrienne and redesign deploy.md based on Eliot's feedback for capture sync (#3984)

Author: sguequierre

docs/data-ai/ai/deploy.md

@@ -28,22 +28,17 @@ aliases:
   - /manage/data/deploy-model/
 ---
 
-The Machine Learning (ML) model service allows you to deploy [machine learning models](/data-ai/ai/deploy/#deploy-your-ml-model) to your machine.
-The service works with models trained inside and outside the Viam app:
-
-- You can [train TFlite](/data-ai/ai/train-tflite/) or [other models](/data-ai/ai/train/) on data from your machines.
-- You can upload externally trained models on the [**MODELS** tab](https://app.viam.com/data/models) in the **DATA** section of the Viam app.
-- You can use [ML models](https://app.viam.com/registry?type=ML+Model) from the [Viam Registry](https://app.viam.com/registry).
-- You can use a [model](/data-ai/ai/deploy/#deploy-your-ml-model) trained outside the Viam platform whose files are on your machine.
+After training or uploading a machine learning model, use a machine learning (ML) model service to deploy the ML model to your machine.
 
-## Deploy your ML model
+## Deploy your ML model on an ML model service
 
-Navigate to the **CONFIGURE** tab of one of your machine in the [Viam app](https://app.viam.com).
-Add an ML model service that supports the ML model you trained or the one you want to use from the registry.
+1. Navigate to the **CONFIGURE** tab of one of your machine in the [Viam app](https://app.viam.com).
+2. Add an ML model service that supports the ML model you want to use.
+   - For example, use the `ML model / TFLite CPU` service for TFlite ML models that you trained with Viam's built-in training.
 
 {{<resources_svc api="rdk:service:mlmodel" type="ML model">}}
 
-### Model framework support
+{{% expand "Want more information about model framework and hardware support for each ML model service? Click here." %}}
 
 Viam currently supports the following frameworks:
 
@@ -59,16 +54,14 @@ Viam currently supports the following frameworks:
 For some models of the ML model service, like the [Triton ML model service](https://github.com/viamrobotics/viam-mlmodelservice-triton/) for Jetson boards, you can configure the service to use either the available CPU or a dedicated GPU.
 {{< /alert >}}
 
-For example,use the `ML model / TFLite CPU` service for TFlite ML models.
-If you used the built-in training, this is the ML model service you need to use.
-If you used a custom training script, you may need a different ML model service.
+{{< /expand>}}
 
-To deploy a model, click **Select model** and select the model from your organization or the registry.
-Save your config.
+3. Click **Select model** and select a model from your organization or the registry.
+4. Save your config.
 
-### Machine learning models from registry
+### Models available to deploy on the ML Model service
 
-You can search the machine learning models that are available to deploy on this service from the registry here:
+You can also use these publicly available machine learning models with an ML model service:
 
 {{<mlmodels>}}
 
@@ -85,7 +78,14 @@ Enter **JSON** mode and find the `"packages"` section of your config.
 Replace `"version": "latest"` with `"version"` from the package reference you just copied, for example `"version": "2024-11-14T15-05-26"`.
 Save your config to use your specified version of the ML model.
 
-## Next steps
+## How the ML model service works
+
+The service works with models trained inside and outside the Viam app:
+
+- You can [train TFlite](/data-ai/ai/train-tflite/) or [other model frameworks](/data-ai/ai/train/) on data from your machines.
+- You can use [ML models](https://app.viam.com/registry?type=ML+Model) from the [Viam Registry](https://app.viam.com/registry).
+- You can upload externally trained models from a model file on the [**MODELS** tab](https://app.viam.com/data/models) in the **DATA** section of the Viam app.
+- You can use a [model](/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) trained outside the Viam platform whose files are on your machine. See the documentation of the model of ML model service you're using (pick one that supports your model framework) for instructions on this.
 
 On its own the ML model service only runs the model.
 After deploying your model, you need to configure an additional service to use the deployed model.

docs/data-ai/ai/train-tflite.md

@@ -53,43 +53,6 @@ Follow the guide to [create a dataset](/data-ai/ai/create-dataset/) if you haven
 
 {{% /expand%}}
 
-{{% expand "A configured camera. Click to see instructions." %}}
-
-First, connect the camera to your machine's computer if it's not already connected (like with an inbuilt laptop webcam).
-
-Then, navigate to the **CONFIGURE** tab of your machine's page in the [Viam app](https://app.viam.com).
-Click the **+** icon next to your machine part in the left-hand menu and select **Component**.
-The `webcam` model supports most USB cameras and inbuilt laptop webcams.
-You can find additional camera models in the [camera configuration](/operate/reference/components/camera/#configuration) documentation.
-
-Complete the camera configuration and use the **TEST** panel in the configuration card to test that the camera is working.
-
-{{% /expand%}}
-
-{{% expand "No computer or webcam?" %}}
-
-No problem.
-You don't need to buy or own any hardware to complete this guide.
-
-Use [Try Viam](https://app.viam.com/try) to borrow a rover free of cost online.
-The rover already has `viam-server` installed and is configured with some components, including a webcam.
-
-Once you have borrowed a rover, go to its **CONTROL** tab where you can view camera streams and also drive the rover.
-You should have a front-facing camera and an overhead view of your rover.
-Now you know what the rover can perceive.
-
-To change what the front-facing camera is pointed at, find the **cam** camera panel on the **CONTROL** tab and click **Toggle picture-in-picture** so you can continue to view the camera stream.
-Then, find the **viam_base** panel and drive the rover around.
-
-Now that you have seen that the cameras on your Try Viam rover work, begin by [Creating a dataset and labeling data](/data-ai/ai/create-dataset/).
-You can drive the rover around as you capture data to get a variety of images from different angles.
-
-{{< alert title="Tip" color="tip" >}}
-Be aware that if you are running out of time during your rental, you can extend your rover rental as long as there are no other reservations.
-{{< /alert >}}
-
-{{% /expand%}}
-
 ## Train a machine learning (ML) model
 
 Now that you have a dataset with your labeled images, you are ready to train a machine learning model.
@@ -163,7 +126,7 @@ Once your model has finished training, you can test it.
 Ideally, you want your ML model to be able to work with a high level of confidence.
 As you test it, if you notice faulty predictions or confidence scores, you will need to adjust your dataset and retrain your model.
 
-If you trained a classification model, you can test it with the following instructions.
+If you trained a _classification_ model, you can test it with the following instructions.
 
 1. Navigate to the [**DATA** tab](https://app.viam.com/data/view) and click on the **Images** subtab.
 1. Click on an image to open the side menu, and select the **Actions** tab.
@@ -180,7 +143,8 @@ You can test both detection models and classifier models using the following res
 
 ## Next steps
 
-Now your machine can make inferences about its environment. The next step is to [act](/data-ai/ai/act/) or [alert](/data-ai/ai/alert/) based on these inferences.
+Now your machine can make inferences about its environment.
+The next step is to [deploy](/data-ai/ai/deploy/) the ML model and then [act](/data-ai/ai/act/) or [alert](/data-ai/ai/alert/) based on these inferences.
 
 See the following tutorials for examples of using machine learning models to make your machine do things based on its inferences about its environment:
 

docs/dev/reference/changelog.md

@@ -273,7 +273,7 @@ In addition to other improvements, your component, service, and other resource c
 {{% changelog date="2024-03-01" color="added" title="Additional ML models" %}}
 
 Viam has added support for the TensorFlow, PyTorch, and ONNX ML model frameworks, expanding upon the existing support for TensorFlow Lite models.
-You can now upload your own ML model(/data-ai/ai/deploy/#deploy-your-ml-model) using any of these frameworks for use with the Vision service.
+You can now upload your own ML model(/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) using any of these frameworks for use with the Vision service.
 
 {{% /changelog %}}
 
@@ -312,7 +312,7 @@ You can now use the [generic service](/operate/reference/components/generic/) to
 
 {{% changelog date="2024-02-12" color="added" title="ML models in the registry" %}}
 
-You can now upload [machine learning (ML) models](/data-ai/ai/deploy/#deploy-your-ml-model) to the Viam Registry, in addition to modules.
+You can now upload [machine learning (ML) models](/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) to the Viam Registry, in addition to modules.
 You may upload models you have trained yourself using the Viam app, or models you have trained outside of the App.
 When uploading, you have the option to make your model available to the general public for reuse.
 
@@ -994,7 +994,7 @@ You will need to first register the machine learning model file with the [ML mod
 {{% changelog date="2023-03-31" color="added" title="Machine learning for image classification models" %}}
 
 You can now [train](/data-ai/ai/train-tflite/) and [deploy](/data-ai/ai/deploy/) image classification models with the [data management service](/data-ai/capture-data/capture-sync/) and use your machine's image data directly within Viam.
-Additionally, you can upload and use existing [machine learning models](/data-ai/ai/deploy/#deploy-your-ml-model) with your machines.
+Additionally, you can upload and use existing [machine learning models](/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) with your machines.
 For more information on using data synced to the cloud to train machine learning models, read [train a TFlite](/data-ai/ai/train-tflite/) or [another model](/data-ai/ai/train/).
 
 {{% /changelog %}}

docs/operate/reference/services/vision/mlmodel.md

@@ -29,7 +29,7 @@ Before configuring your `mlmodel` detector or classifier, you need to:
 
 <h4>1. Train or upload an ML model</h4>
 
-You can add an [existing model](/data-ai/ai/deploy/#deploy-your-ml-model) or [train a TFlite](/data-ai/ai/train-tflite/) or [another model](/data-ai/ai/train/) for object detection and classification using your data in the [Viam Cloud](/data-ai/capture-data/capture-sync/).
+You can add an [existing model](/data-ai/ai/deploy/#deploy-your-ml-model-on-an-ml-model-service) or [train a TFlite](/data-ai/ai/train-tflite/) or [another model](/data-ai/ai/train/) for object detection and classification using your data in the [Viam Cloud](/data-ai/capture-data/capture-sync/).
 
 {{% /manualcard %}}
 {{% manualcard %}}