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 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 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 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 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 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 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 how to configure feature-based explanations on a pre-trained image classification model and make online and batch predictions with explanations.

The steps performed include:

- Download pretrained model from TensorFlow Hub
- Upload model for deployment
- Deploy model for online prediction
- Make online prediction with explanations
- Make batch predictions with explanations

Learn how to configure feature-based explanations using **sampled Shapley method** on a TensorFlow text classification model for online predictions with explanations.

The steps performed include:

- Build and train a TensorFlow text classification model
- Upload model for deployment
- Deploy model for online prediction
- Make online prediction with explanations