Train an AutoML Edge model using the Google Cloud console

You create an AutoML Edge (exportable) model directly in the UI for certain data types, or by starting a training pipeline job programmatically. You create this model using a prepared dataset. Create this dataset in the Google Cloud console or using the API. Vertex AI API uses the items from the dataset to train the model, test it, and evaluate model performance. Review the evaluations results, adjust the training dataset as needed, and create a new training job using the improved dataset.

Training jobs can take several hours to complete. The Vertex AI page of the Google Cloud console shows the status of training.

Training an AutoML Edge model

  1. In the Google Cloud console, in the Vertex AI section, go to the Datasets page.

    Go to the Datasets page

  2. Click the name of the dataset you want to use to train your model to open its details page.

  3. If your data type uses annotation sets, select the annotation set you want to use for this model.

  4. Click Train new model.

  5. In the Train new model page, complete the following steps for your data type:

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    1. Select AutoML Edge for the training method and click Continue.

    2. Enter the display name for your new model.

    3. If you want manually set how your training data is split, expand Advanced options and select a data split option. Learn more.

    4. Click Continue.

    5. Classification models only (optional): In the Explainability section, select Generate explainable bitmaps for each image in the test set to enable Vertex Explainable AI. Choose visualization settings and click Continue.

      This feature has costs associated with it. See Pricing for more information.

    6. Select the optimization goal that best suits your need. You can optimize for accuracy, latency, or both.

    7. Click Continue.

    8. In the Compute and pricing window, enter the maximum number of hours you want your model to train for.

      This setting helps you put a cap on the training costs. The actual time elapsed can be longer than this value, because there are other operations involved in creating a new model.

    9. If you want to stop training when the model is no longer improving, select Enable early stopping.

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    1. Enter the display name for your new model.

    2. Click Continue.

    3. Select AutoML Edge for the training method and click Continue.

    4. Select the optimization goal that best suits your need. You can optimize for accuracy, latency, or both.

    5. Click Continue.

      Several minutes after training starts, you can check the training node hour estimation from the model's properties information. If you cancel the training, there is no charge on the current product.

  6. Click Start Training.

    Model training can take many hours, depending on your training budget (image only) and the size and complexity of your data. You can close this tab and return to it later. You will receive an email when your model has completed training.

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