Train and Monitor an ML Model
A 4-step recipe with Viam. We can help you get access.
Recipe · 4 steps
Train and Monitor an ML Model
with Viam · Artificial Intelligence Management
Submit a training job, check status, and branch to logs or cancellation.
- Submit a built-in TFLite training job against the dataset.
- Retrieve the training job by the returned id to inspect its status.
- Pull the training job logs while the job is pending or running.
- Cancel the training job when it has failed to free resources.
What you'll need to have ready
- apiKey: Viam API key value sent in the key header.
- organizationId: The organization the training job runs under.
- datasetId: The dataset id the model is trained against.
- modelName: Name for the resulting model.
- modelType: One of single_label_classification, multi_label_classification, object_detection.
For your developer
Arazzo workflow viam:train-and-monitor-model, 4 operations: submitTrainingJob → getTrainingJob → getTrainingJobLogs → cancelTrainingJob. Specs on APIs.io.