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[AutoML Tabular Training and Prediction](official/automl/automl-tabular-classification.ipynb)
Learn how to train and make predictions on an AutoML model based on a tabular dataset.
The steps performed include the following:
- Create a Vertex AI model training job.
- Train an AutoML Tabular model.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction by sending data.
- Undeploy the `Model` resource.
[Create, train, and deploy an AutoML text classification model](official/automl/automl-text-classification.ipynb)
Learn how to use `AutoML` to train a text classification model.
The steps performed include:
* Create a `Vertex AI Dataset`.
* Train an `AutoML` text classification `Model` resource.
* Obtain the evaluation metrics for the `Model` resource.
* Create an `Endpoint` resource.
* Deploy the `Model` resource to the `Endpoint` resource.
* Make an online prediction
* Make a batch prediction
[AutoML training video classification model for batch prediction](official/automl/sdk_automl_video_classification_batch.ipynb)
Learn how to create an AutoML video classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
[AutoML tabular forecasting model for batch prediction](official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.
The steps performed include:
- Create a `Vertex AI Dataset` resource.
- Train an `AutoML` tabular forecasting `Model` resource.
- Obtain the evaluation metrics for the `Model` resource.
- Make a batch prediction.
[AutoML training image object detection model for batch prediction](official/automl/sdk_automl_image_object_detection_batch.ipynb)
Learn how to create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
[AutoML training video action recognition model for batch prediction](official/automl/sdk_automl_video_action_recognition_batch.ipynb)
Learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
[AutoML training text sentiment analysis model for online prediction](official/automl/sdk_automl_text_sentiment_analysis_online.ipynb)
Learn how to create an AutoML text sentiment analysis model and deploy for online prediction from a Python script using the Vertex SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Create a training job for the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
[Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
Learn how to create an BQML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.
The steps performed are:
- Train the BQML ARIMA_PLUS model.
- View BQML model evaluation.
- Make a batch prediction with the BQML model.
- Create a Vertex AI `Dataset` resource.
- Train the Vertex AI Forecasting model.
- View the Model evaluation.
- Make a batch prediction with the Model.
[AutoML training tabular regression model for online prediction using BigQuery](official/automl/sdk_automl_tabular_regression_online_bq.ipynb)
Learn how to create an AutoML tabular regression model and deploy for online prediction from a Python script using the Vertex AI SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.