DOCS-3204: Add docs for add image to dataset button, incorporate into tutorials where helpful

assets/scss/_styles_project.scss

@@ -3369,6 +3369,20 @@ div.tablestep > table td, div.tablestep table td {
   padding-left: 1rem;
 }
 
+/* ensure that, up to a reasonable 4 levels of nesting, including tablesteps, we use distinct ordered list labeling schemes */
+.tablestep ol, ol ol {
+ list-style-type: upper-alpha;
+}
+
+.tablestep ol ol, ol ol ol {
+ list-style-type: lower-roman;
+}
+
+.tablestep ol ol ol, ol ol ol ol {
+ list-style-type: lower-alpha;
+}
+/* end ordered list labeling styling */
+
 .td-content > .code-toolbar, .td-content * .code-toolbar {
   margin-top: 1rem;
 }

docs/data-ai/ai/run-inference.md

@@ -43,7 +43,7 @@ Then, from the **Select model** dropdown, select the name of the ML model servic
 
 ### Test your changes
 
-You can test a deployed vision service by clicking on the **Test** area of its configuration panel or from the [**CONTROL** tab](/manage/troubleshoot/teleoperate/default-interface/#viam-app).
+You can test a deployed vision service by clicking on the **Test** area of its configuration panel or from the [**CONTROL** page](/manage/troubleshoot/teleoperate/default-interface/#viam-app).
 
 The camera stream shows when the vision service identifies something.
 Try pointing the camera at a scene similar to your training data.
@@ -59,7 +59,7 @@ Start by setting the value to 0.8.
 This reduces your output by filtering out anything below a threshold of 80% confidence.
 You can adjust this attribute as necessary.
 
-Click the **Save** button in the top right corner of the page to save your configuration, then close and reopen the **Test** panel of the vision service configuration panel.
+Click the **Save** button in the top right corner of the page to save your configuration, then close and reopen the **TEST** panel of the vision service configuration panel.
 Now if you reopen the panel, you will only see classifications or detections with a confidence value higher than the `default_minimum_confidence` attribute.
 
 {{< /expand>}}
@@ -69,8 +69,7 @@ For more detailed information, including optional attribute configuration, see t
 ## Use an SDK
 
 You can also run inference using a Viam SDK.
-You can use the [`Infer`](/dev/reference/apis/services/ml/#infer)
-method of the ML Model API to make inferences.
+Use the [`Infer`](/dev/reference/apis/services/ml/#infer) method of the ML Model API to make inferences.
 
 For example:
 

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

@@ -26,53 +26,46 @@ date: "2024-12-03"
 ---
 
 Many machines have cameras through which they can monitor their environment.
-With machine leaning, you can train models on patterns within that visual data.
-You can collect data from the camera stream and label any patterns within the images.
-
-If a camera is pointed at a food display, for example, you can label the image of the display with `full` or `empty`, or label items such as individual `pizza_slice`s.
-
-Using a model trained on such images, machines can make inferences about their environments.
-Your machines can then automatically trigger alerts or perform other actions.
-If a food display is empty, the machine could, for example, alert a supervisor to restock the display.
-
-Common use cases for this are **security**, **quality assurance**, and **food service** applications.
-
+With machine leaning (ML), you can train models on patterns within image data.
 Follow this guide to use your image data to train an ML model, so that your machine can make inferences about its environment.
 
 ## Prerequisites
 
-{{% expand "A running machine connected to the Viam app. Click to see instructions." %}}
+{{% expand "a machine connected to the Viam app" %}}
 
 {{% snippet "setup.md" %}}
 
-{{% /expand%}}
+{{% /expand %}}
 
-{{% expand "A dataset with labels. Click to see instructions." %}}
+{{% expand "a dataset with labels" %}}
 
-Follow the guide to [create a dataset](/data-ai/ai/create-dataset/) if you haven't already.
+Follow the guide to [create a dataset](/data-ai/ai/create-dataset/).
 
 {{% /expand%}}
 
-## Train a machine learning (ML) model
+## Train a machine learning model
 
-Now that you have a dataset with your labeled images, you are ready to train a machine learning model.
+Now that you have a dataset that contains your labeled images, you are ready to train a machine learning model.
 
 {{< table >}}
 {{% tablestep number=1 %}}
-**Train an ML model**
+**Find your training dataset**
 
 In the Viam app, navigate to your list of [**DATASETS**](https://app.viam.com/data/datasets) and select the one you want to train on.
 
-Click **Train model** and follow the prompts.
+{{% /tablestep %}}
+{{% tablestep number=2 %}}
+**Train an ML model**
 
+Click **Train model** and follow the prompts.
 You can train a TFLite model using **Built-in training**.
 
-Click **Next steps**.
+{{<imgproc src="/services/ml/train-model.png" resize="1200x" declaredimensions=true style="width:500px" alt="The shapes dataset." class="imgzoom fill shadow" >}}
 
-{{<imgproc src="/tutorials/data-management/shapes-dataset.png" resize="1200x" declaredimensions=true style="width:500px" alt="The shapes dataset." class="imgzoom fill shadow" >}}
+Click **Next steps**.
 
 {{% /tablestep %}}
-{{% tablestep number=2 %}}
+{{% tablestep number=3 %}}
 **Fill in the details for your ML model**
 
 Enter a name for your new model.
@@ -91,7 +84,7 @@ Click **Train model**.
 {{< imgproc src="/tutorials/data-management/train-model.png" alt="The data tab showing the train a model pane" style="width:500px" resize="1200x" class="imgzoom fill shadow" >}}
 
 {{% /tablestep %}}
-{{% tablestep number=3 %}}
+{{% tablestep number=4 %}}
 **Wait for your model to train**
 
 The model now starts training and you can follow its process on the [**TRAINING** tab](https://app.viam.com/training).
@@ -101,7 +94,7 @@ Once the model has finished training, it becomes visible on the [**MODELS** tab]
 You will receive an email when your model finishes training.
 
 {{% /tablestep %}}
-{{% tablestep number=4 %}}
+{{% tablestep number=5 %}}
 **Debug your training job**
 
 From the [**TRAINING** tab](https://app.viam.com/training), click on your training job's ID to see its logs.
@@ -128,10 +121,10 @@ As you test it, if you notice faulty predictions or confidence scores, you will
 
 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. Navigate to the [**DATA** page](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.
 1. In the **Run model** section, select your model and specify a confidence threshold.
-1. Click **Run model**
+1. Click **Run model**.
 
 If the results exceed the confidence threshold, the **Run model** section shows a label and the responding confidence threshold.
 
@@ -141,6 +134,23 @@ You can test both detection models and classifier models using the following res
 - [a `tflite_cpu` ML model](/data-ai/ai/deploy/) with the model you just trained
 - [an `mlmodel` vision service](/operate/reference/services/vision/mlmodel/) using the `tflite_cpu` model
 
+## Iterate on your ML model
+
+With the right training dataset, your ML model can perform well as soon as training completes.
+However, edge cases always exist.
+You are unlikely to account for all false positives or false negatives during your first round of training.
+Instead, keep track of edge cases as you run your model.
+Add images capturing edge cases to your training dataset, annotate them, and re-train your model using the new data.
+Using this approach, each subsequent model version becomes more accurate than the last.
+
+To capture images of edge cases and re-train your model using those images, complete the following steps:
+
+1. Add edge case images to your training dataset. You can find edge cases in your existing data on the [**DATA** page](https://app.viam.com/data/) or [capture new images and add them to your training dataset](/data-ai/ai/create-dataset/#capture-images).
+
+1. Visit the **DATASET** tab of the **DATA** page and annotate the image.
+
+1. Repeat the [steps above](/data-ai/ai/train-tflite/#train-a-machine-learning-model) to train and release a new version of your ML model. Your machines will automatically update to the new version of the model soon after release.
+
 ## Next steps
 
 Now your machine can make inferences about its environment.

docs/data-ai/ai/train.md

@@ -17,7 +17,7 @@ description: "If you want to train models to custom specifications, write a cust
 date: "2024-12-04"
 ---
 
-You can create custom Python training scripts that train ML models to your specifications using PyTorch, Tensorflow, TFLite, ONNX, or any other Machine Learning framework.
+You can create custom Python training scripts that train machine learning models to your specifications using PyTorch, TensorFlow, TFLite, ONNX, or any other ML framework.
 Once you upload a training script to the [Viam Registry](https://app.viam.com/registry?type=Training+Script), you can use it to build ML models in the Viam Cloud based on your datasets.
 
 You can also use training scripts that are in the registry already.
@@ -27,9 +27,9 @@ If you wish to do this, skip to [Submit a training job](#submit-a-training-job).
 
 {{% expand "A dataset with data you can train an ML model on. Click to see instructions." %}}
 
-For image data, you can follow the instructions to [Create a dataset](/data-ai/ai/create-dataset/) to create a dataset and label data.
+For images, follow the instructions to [Create a dataset](/data-ai/ai/create-dataset/) to create a dataset and label data.
 
-For other data you can use the [Data Client API](/dev/reference/apis/data-client/) from within the training script to get data stored in the Viam Cloud.
+For other data, use the [Data Client API](/dev/reference/apis/data-client/) from within the training script to store data in the Viam Cloud.
 
 {{% /expand%}}
 
@@ -61,9 +61,9 @@ my-training/
 {{% tablestep number=2 %}}
 **Add `setup.py` code**
 
-Add the following code to `setup.py` and add additional required packages on line 11:
+Add the following code to `setup.py`:
 
-```python {class="line-numbers linkable-line-numbers" data-line="9"}
+```python {class="line-numbers linkable-line-numbers"}
 from setuptools import find_packages, setup
 
 setup(
@@ -78,13 +78,7 @@ setup(
 ```
 
 {{% /tablestep %}}
-{{% tablestep number=3 %}}
-**Create `__init__.py`**
-
-If you haven't already, create a folder called <file>model</file> and create an empty file inside it called <file>\_\_init\_\_.py</file>.
-
-{{% /tablestep %}}
-{{< tablestep number=4 >}}
+{{< tablestep number=3 >}}
 
 <p><strong>Add <code>training.py</code> code</strong></p>
 
@@ -529,7 +523,7 @@ if __name__ == "__main__":
 {{% /expand %}}
 
 {{% /tablestep %}}
-{{< tablestep number=5 >}}
+{{< tablestep number=4 >}}
 
 <p><strong>Understand template script parsing functionality</strong></p>
 <p>When a training script is run, the Viam platform passes the dataset file for the training and the designated model output directory to the script.</p>
@@ -552,7 +546,7 @@ You can add additional custom command line inputs by adding them to the `parse_a
 
 {{% /expand %}}
 
-{{% expand "Click for more information on parsing annotations from dataset file." %}}
+{{% expand "Click for more information on parsing annotations from the dataset file." %}}
 
 When you submit a training job to the Viam Cloud, Viam will pass a `dataset_file` to the training script when you train an ML model with it.
 The file contains metadata from the dataset used for the training, including the file path for each data point and any annotations associated with the data.
@@ -624,46 +618,46 @@ Dataset JSON files for image datasets with bounding box labels and classificatio
 }
 ```
 
-In your training script, you must parse the dataset file for the classification or bounding box annotations from the dataset metadata.
+In your training script, you must parse the dataset file to extract classification or bounding box annotations from the dataset metadata.
 Depending on if you are training a classification or detection model, the template script contains the `parse_filenames_and_labels_from_json()` and the `parse_filenames_and_bboxes_from_json()` function.
 
 {{% /expand%}}
 
 <p>If the script you are creating does not use an image dataset, you only need the model output directory.</p>
 
 {{% /tablestep %}}
-{{% tablestep number=6 %}}
+{{% tablestep number=5 %}}
 **Add logic to produce the model artifact**
 
-You must fill in the `build_and_compile_model` function.
-In this part of the script, you use the data from the dataset and the annotations from the dataset file to build a Machine Learning model.
+Fill in the `build_and_compile_model` function.
+In this part of the script, you use data and annotations from the dataset file to build an ML model.
 
 As an example, you can refer to the logic from <file>model/training.py</file> from this [example classification training script](https://github.com/viam-modules/classification-tflite) that trains a classification model using TensorFlow and Keras.
 
 {{% /tablestep %}}
-{{% tablestep number=7 %}}
+{{% tablestep number=6 %}}
 **Save the model artifact**
 
-The `save_model()` and the `save_labels()` functions in the template before the `main` logic save the model artifact your training job produces to the `model_output_directory` in the cloud.
+In this example template, the training job produces a model artifact.
+The `save_model()` and `save_labels()` functions save that model artifact to the `model_output_directory`.
 
-Once a training job is complete, Viam checks the output directory and creates a package with all of the contents of the directory, creating or updating a registry item for the ML model.
+When the training job completes, Viam checks the output directory for the model artifact, packages the model, and uploads it to the registry.
 
-You must fill in these functions.
+You must fill in the `save_model()` and `save_labels()` functions.
 
-As an example, you can refer to the logic from <file>model/training.py</file> from this [example classification training script](https://github.com/viam-modules/classification-tflite) that trains a classification model using TensorFlow and Keras.
+As an example, refer to the logic from <file>model/training.py</file> from this [example classification training script](https://github.com/viam-modules/classification-tflite) that trains a classification model using TensorFlow and Keras.
 
 {{% /tablestep %}}
-{{% tablestep number=8 %}}
+{{% tablestep number=7 %}}
 **Update the main method**
 
 Update the main to call the functions you have just created.
 
 {{% /tablestep %}}
-{{% tablestep number=9 %}}
-**Using Viam APIs in a training script**
+{{% tablestep number=8 %}}
+**Use Viam APIs in a training script**
 
-If you need to access any of the [Viam APIs](/dev/reference/apis/) within a custom training script, you can use the environment variables `API_KEY` and `API_KEY_ID` to establish a connection.
-These environment variables will be available to training scripts.
+To access [Viam APIs](/dev/reference/apis/) within a custom training script, use the environment variables `API_KEY` and `API_KEY_ID` to establish a connection.
 
 ```python
 async def connect() -> ViamClient:
@@ -807,13 +801,13 @@ For example:
 viam train submit custom from-registry --dataset-id=<INSERT DATASET ID> \
   --org-id=<INSERT ORG ID> --model-name=MyRegistryModel \
   --model-version=2 --version=1 \
-  --script-name=mycompany:MyCustomTrainingScript
+  --script-name=mycompany:MyCustomTrainingScript \
   --args=custom_arg1=3,custom_arg2="'green_square blue_star'"
 ```
 
 This command submits a training job to the previously uploaded `MyCustomTrainingScript` with another input dataset, which trains `MyRegistryModel` and publishes that to the registry.
 
-You can get the dataset id from the dataset page or using the [`viam dataset list`](/dev/tools/cli/#dataset) command.
+You can get the dataset id from the **DATASET** tab of the **DATA** page or by running the [`viam dataset list`](/dev/tools/cli/#dataset) command.
 
 {{% /tab %}}
 {{< /tabs >}}

docs/data-ai/reference/mlmodel-design.md

@@ -15,7 +15,7 @@ aliases:
   - /operate/reference/advanced-modules/mlmodel-design/
 ---
 
-The [Machine Learning (ML) model service](/data-ai/ai/deploy/) allow you to deploy machine learning models to your smart machine.
+The [Machine Learning (ML) model service](/data-ai/ai/deploy/) allows you to deploy machine learning models to your smart machine.
 Vision services, like [an `"mlmodel"` detector](/dev/reference/apis/services/vision/#detections) or [classifier](/dev/reference/apis/services/vision/#classifications), enable your machines to identify and classify objects in images with the deployed models' predictions.
 
 The two services work closely together, with the vision service relying on the deployed ML model to make inferences.

docs/dev/reference/apis/data-client.md

@@ -128,11 +128,3 @@ Once you have instantiated a `DataClient`, you can run [API methods](#api) again
 {{< readfile "/static/include/app/apis/generated/data.md" >}}
 
 {{< readfile "/static/include/app/apis/generated/dataset.md" >}}
-
-## Find part ID
-
-To copy the ID of your machine part, select the part status dropdown to the right of your machine's location and name on the top of its page and click the copy icon next to **Part ID**.
-
-For example:
-
-{{<imgproc src="/build/program/data-client/grab-part-id.png" resize="1000x" class="shadow imgzoom" style="width: 500px" declaredimensions=true alt="Part ID displayed in the Viam app.">}}

docs/dev/reference/glossary/pin-number.md

@@ -11,4 +11,4 @@ This number is distinct from the GPIO number assigned to general purpose input/o
 For example, pin number "11" on a NVIDIA Jetson Nano is GPIO "50", and pin number "11" on a Raspberry Pi 4 is GPIO "17".
 When Viam documentation refers to pin number, it will always mean the pin's physical index and not GPIO number.
 
-Pin numbers are found on a board's pinout diagram and data sheet.
+Pin numbers are found on a board's pinout diagram and datasheet.

docs/operate/reference/architecture/parts.md

@@ -58,6 +58,14 @@ If it cannot connect to them, the part will still successfully start up.
 
 ![Example of a remote and a two part machine where the main (and only) part of machine 1 remotes into the main part of machine 2, and thus has access to all resources of machine 2.](/build/configure/parts/remotes-diagram.png)
 
+### Find part ID
+
+To copy the ID of your machine part, select the part status dropdown to the right of your machine's location and name on the top of its page and click the copy icon next to **Part ID**.
+
+For example:
+
+{{<imgproc src="/build/program/data-client/grab-part-id.png" resize="1000x" class="shadow imgzoom" style="width: 500px" declaredimensions=true alt="Part ID displayed in the Viam app.">}}
+
 ## Configuration
 
 ### Configure a sub-part

docs/operate/reference/components/camera/webcam.md

@@ -258,14 +258,14 @@ sudo reboot
 {{% /expand%}}
 
 {{% expand "Images are dim on start up" %}}
-If you are capturing camera data, it can happen that the camera captures and syncs miscolored or dark images upon start up.
+If you are capturing camera data, it can happen that the camera captures and syncs discolored or dark images upon start up.
 {{% /expand%}}
 
 {{% expand "CSI Camera not working on a Raspberry Pi" %}}
 
 If you are using a CSI camera v1.3 or v2.0, or v3.0, use the [`viam:camera:csi` module](https://github.com/viamrobotics/csi-camera/) instead.
 
-For Raspberry Pi AI cameras like the IMX500 AI camera, you can use a module such as [this `viam-pi-ai-camera` vision service](https://github.com/HipsterBrown/viam-pi-ai-camera).
+For Raspberry Pi AI cameras like the IMX500 AI camera, use a module such as [this `viam-pi-ai-camera` vision service](https://github.com/HipsterBrown/viam-pi-ai-camera).
 For more information about the vision service, see [run inference](https://docs.viam.com/data-ai/ai/run-inference/).
 {{% /expand%}}