AutoML Tabular training and prediction
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.
Learn more about Classification for tabular data.
Create, train, and deploy an AutoML text classification model
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
Learn more about Classification for text data.
Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS
Learn how to create an BigQuery ML ARIMA_PLUS model using a training Vertex AI Pipeline from Google Cloud Pipeline Components , and then do a batch prediction using the corresponding prediction pipeline.
The steps performed are:
- Train the BigQuery ML ARIMA_PLUS model.
- View BigQuery ML model evaluation.
- Make a batch prediction with the BigQuery ML model.
- Create a Vertex AI `Dataset` resource.
- Train the Vertex AI Forecasting model.
- View the Vertex AI Model Evaluation results.
- Make a batch prediction with the Vertex AI Forecasting model.
Learn more about BQML ARIMA+ forecasting for tabular data.
AutoML training image classification model for batch prediction
In this tutorial, you create an AutoML image classification model from a Python script, and then do a batch prediction using the Vertex SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
Learn more about Get predictions from an image classification model.
AutoML training image classification model for online prediction
In this tutorial, you create an AutoML image classification model and deploy for online prediction from a Python script using the Vertex 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`.
Learn more about Get predictions from an image classification model.
AutoML training image object detection model for export to edge
In this tutorial, you create an AutoML image object detection model from a Python script using the Vertex SDK, and then export the model as an Edge model in TFLite format.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- Export the `Edge` model from the `Model` resource to Cloud Storage.
- Download the model locally.
- Make a local prediction.
AutoML training image object detection model for online prediction
In this tutorial, you create an AutoML image object detection 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`.
Learn more about Object detection for image data.
AutoML Tabular Workflow pipelines
Learn how to create two regression models using Vertex AI Pipelines downloaded from Google Cloud Pipeline Components .
The steps performed are:
- Create a training pipeline that reduces the search space from the default to save time.
- Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.
Learn more about Tabular Workflow for E2E AutoML.
AutoML training text entity extraction model for batch prediction
In this tutorial, you create an AutoML text entity extraction 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.
Learn more about Entity extraction for text data.
AutoML training text sentiment analysis model for batch prediction
In this tutorial, you create an AutoML text sentiment analysis model from a Python script, and then do a batch prediction using the Vertex SDK.
The steps performed include:
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
Get started with AutoML Training
Learn how to use `AutoML` for training with `Vertex AI`.
The steps performed include:
- Train an image model
- Export the image model as an edge model
- Train a tabular model
- Export the tabular model as a cloud model
- Train a text model
- Train a video model
Learn more about AutoML training.
AutoML training hierarchical forecasting for batch prediction
In this tutorial, you create an AutoML hierarchical forecasting model and deploy it for batch prediction using the Vertex AI SDK for Python.
The steps performed include:
- Create a Vertex AI `TimeSeriesDataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
Learn more about Hierarchical forecasting for tabular data.
AutoML training image object detection model for batch prediction
In this tutorial, you 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.
Learn more about Object detection for image data.
AutoML tabular forecasting model for batch prediction
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.
Learn more about Forecasting for tabular data.
AutoML training tabular regression model for batch prediction using BigQuery
Learn how to create an AutoML tabular regression model and deploy it for batch prediction using the Vertex AI SDK for Python.
The steps performed include:
- Create a Vertex AI `Dataset` resource.
- Train the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
Learn more about Regression for tabular data.
AutoML training tabular regression model for online prediction using BigQuery
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`.
Learn more about Regression for tabular data.
AutoML training text entity extraction model for online prediction
Learn how to create an AutoML text entity extraction 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`.
Learn more about Entity extraction for text data.
Training an AutoML text sentiment analysis model for online predictions
Learn how to create an AutoML text sentiment analysis model and deploy it for online predictions from a Python script using the Vertex AI SDK.
The steps performed include:
- Create a `Vertex AI Dataset` resource.
- Create a training job for the AutoML model on the dataset.
- View the model evaluation metrics.
- Deploy the `Vertex AI Model` resource to a serving `Vertex AI Endpoint`.
- Make a prediction request to the deployed model.
- Undeploy the model from endpoint.
- Perform clean up process.
Learn more about Sentiment analysis for text data.
AutoML training video action recognition model for batch prediction
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 AI Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
Learn more about Action recognition for video data.
AutoML training video classification model for batch prediction
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.
Learn more about Classification for video data.
AutoML training video object tracking model for batch prediction
Learn how to create an AutoML video object tracking 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.
Learn more about Object tracking for video data.