model_garden/notebooks/official/explainable_ai

AutoML training tabular binary classification model for batch explanation

Learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Batch Prediction` to make predictions with explanations.

The steps performed include:

- Create a `Vertex Dataset` resource.
- Train an `AutoML` tabular binary classification model.
- View the model evaluation metrics for the trained model.
- Make a batch prediction request with explainability.

   Learn more about AutoML Tabular.

   Learn more about Vertex Explainable AI.

AutoML training tabular classification model for online explanation

Learn how to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations.

The steps performed include:

- Create a `Vertex Dataset` resource.
- Train an `AutoML` tabular binary classification model.
- View the model evaluation metrics for the trained model.
- Create a serving `Endpoint` resource.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make an online prediction request with explainability.
- Undeploy the `Model` resource.

   Learn more about AutoML Tabular.

   Learn more about Vertex Explainable AI.

Custom training image classification model for batch prediction with explainabilty

Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.

The steps performed include:

- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanation parameters as a `Model` resource.
- Make a batch prediction with explanations.

   Learn more about Vertex Explainable AI.

   Learn more about Vertex AI Batch Prediction.

Custom training image classification model for online prediction with explainabilty

Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.

The steps performed include:

- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanations as a `Model` resource.
- Create a serving `Endpoint` resource.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction with explanation.
- Undeploy the `Model` resource.

   Learn more about Vertex Explainable AI.

   Learn more about Vertex AI Prediction.

Custom training tabular regression model for batch prediction with explainabilty

Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.

The steps performed include:

- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanations as a `Model` resource.
- Make a batch prediction with explanations.

   Learn more about Vertex Explainable AI.

   Learn more about Vertex AI Batch Prediction.

Custom training tabular regression model for online prediction with explainabilty

Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.

The steps performed include:

- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanations as a `Model` resource.
- Create a serving `Endpoint` resource.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction with explanation.
- Undeploy the `Model` resource.

   Learn more about Vertex Explainable AI.

   Learn more about Vertex AI Prediction.

Custom training tabular regression model for online prediction with explainabilty using get_metadata

Learn how to create a custom model from a Python script in a Google prebuilt Docker container using the Vertex AI SDK, and then do a prediction with explanations on the deployed model by sending data.

The steps performed include:

- Create a Vertex custom job for training a model.
- Train a TensorFlow model.
- Retrieve and load the model artifacts.
- View the model evaluation.
- Set explanation parameters.
- Upload the model as a Vertex `Model` resource.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction with explanation.
- Undeploy the `Model` resource.

   Learn more about Vertex Explainable AI.

   Learn more about Vertex AI Prediction.