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Train other models
After training or uploading a machine learning model, use a machine learning (ML) model service to deploy the ML model to your machine.
ML model / TFLite CPU
service for TFlite ML models that you trained with Viam’s built-in training.For configuration information, click on the model name:
You can also use these publicly available machine learning models with an ML model service:
When you add a model to the ML model service in the app interface, it automatically grabs the latest version.
You can still edit what version of an ML model your machine uses, but not through the UI.
To deploy a specific version of an ML model, you must edit the raw JSON of your machine.
Go to the Models page on the DATA tab.
Click the > icon to expand the versions of a model and click the … menu on your desired version.
Click Copy package JSON.
Then, return to your machine page.
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.
The service works with models trained inside and outside the Viam app:
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.
For example, you can configure an mlmodel
vision service to visualize the inferences your model makes.
Follow our docs to run inference to add an mlmodel
vision service and see inferences.
For other use cases, consider creating custom functionality with a module.
ML models must be designed in particular shapes to work with the mlmodel
classification or detection model of Viam’s vision service.
See ML Model Design to design a modular ML model service with models that work with vision.
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