diff --git a/docs/ml/_index.md b/docs/ml/_index.md index 431e4b227b..4cac1d928f 100644 --- a/docs/ml/_index.md +++ b/docs/ml/_index.md @@ -15,7 +15,7 @@ menuindent: true {{}} -Viam includes a built-in [machine learning (ML) service](/ml/) which provides your machine with the ability to learn from data and adjust its behavior based on insights gathered from that data. +Viam includes a built-in [machine learning (ML) model service](/ml/) which provides your machine with the ability to learn from data and adjust its behavior based on insights gathered from that data. Common use cases include: - Object detection and classification which enable machines to detect people, animals, plants, or other objects with bounding boxes, and to perform actions when they are detected. @@ -36,33 +36,33 @@ Viam natively supports [TensorFlow Lite](https://www.tensorflow.org/lite) ML mod {{}} - 2. Create a Dataset and Label + 2. Create a dataset and label

Once you have collected data, label your data and create a dataset in preparation for training machine learning models.

{{}} 3. Train or upload an ML model -

Use your labeled data to train your own models for object detection and classification using data from the data management service or add an existing model.

+

Use your labeled data to train your own model for object detection or classification. If you don't want to train your own model, you can also use an ML model from the registry or upload an existing model.

4. Deploy your ML model -

To make use of ML models with your machine, use the built-in ML model service to deploy and run the model.

+

To use ML models with your machine, you must first deploy the model using the built-in ML model service. The ML model service will run the model and allow the vision service to use it.

{{}} - 5. Configure a service -

For object detection and classification, you can use the vision service, which provides an ml model detector and an ml model classifier model.

-

For other usage, you can use a modular resource to integrate it with your machine.

+ 5. Configure a vision service +

For object detection and classification, use the mlmodel detector or the mlmodel classifier from the vision service. The mlmodel vision service uses the ML model that you deployed with the ML model service in step 4.

+

If you have another use case, you can use a modular resource to create a custom ML model service or a custom vision service for your machine.

{{}} 6. Test your detector or classifier -

Test your mlmodel detector or classifier.

+

Follow the instructions to test your mlmodel detector or classifier.