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@@ -99,71 +99,71 @@ The steps performed include:
[AutoML tabular binary classification model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)
<blockquote>
In this tutorial, you learn 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.
In this tutorial, you learn 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.
This tutorial uses the following Google Cloud ML services:
- Vertex AI AutoML
- Vertex AI Prediction
- Vertex Explainable AI
- Vertex AI Model resource
- Vertex AI Endpoint resource
- `Vertex AI AutoML`
- `Vertex AI Prediction`
- `Vertex Explainable AI`
- `Vertex AI Model` resource
- `Vertex AI Endpoint` resource
The steps performed include:
- Create a Vertex Dataset resource.
- Train an AutoML tabular binary classification model.
- Create a `Vertex AI 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.
- 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.
- Undeploy the `Model` resource.
</blockquote>
[Custom tabular regression model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
<blockquote>
In this tutorial, you 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.
In this tutorial, you 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.
This tutorial uses the following Google Cloud ML services:
- Vertex AI Training
- Vertex AI Batch Prediction
- Vertex Explainable AI
- Vertex AI Model resource
- `Vertex AI Training`
- `Vertex AI Batch Prediction`
- `Vertex Explainable AI`
- `Vertex AI Mode`l resource
The steps performed include:
- Create a Vertex AI custom job for training a TensorFlow model.
- Create a `Vertex A`I 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.
- Upload the trained model artifacts and explanations as a `Model` resource.
- Make a batch prediction with explanations.
</blockquote>
[Custom tabular regression model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb)
<blockquote>
In this tutorial, you 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 Prediction to make an online prediction request with explanations.
In this tutorial, you 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 Prediction` to make an online prediction request with explanations.
This tutorial uses the following Google Cloud ML services:
- Vertex AI Training
- Vertex AI Prediction
- Vertex Explainable AI
- Vertex AI Model resource
- Vertex AI Endpoint resource
- `Vertex AI Training`
- `Vertex AI Prediction`
- `Vertex Explainable AI`
- `Vertex AI Model` resource
- `Vertex AI Endpoint` resource
The steps performed include:
- Create a Vertex AI custom job for training a TensorFlow model.
- 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.
- 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.
- Undeploy the `Model` resource.
</blockquote>
[Custom image classification model with batch predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
@@ -179,21 +179,21 @@ The steps performed include:
[Monitoring drift detection in online serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
<blockquote>
In this notebook, you learn to use the Vertex AI Model Monitoring service to detect drift and anomalies in prediction requests from a deployed Vertex AI Model resource.
In this notebook, you learn to use the `Vertex AI Model Monitoring` service to detect drift and anomalies in prediction requests from a deployed `Vertex AI Model` resource.
This tutorial uses the following Google Cloud ML services:
- Vertex AI Model Monitoring
- Vertex AI Prediction
- Vertex AI Model resource
- Vertex AI Endpoint resource
- `Vertex AI Model Monitoring`
- `Vertex AI Prediction`
- `Vertex AI Model` resource
- `Vertex AI Endpoint` resource
The steps performed include:
- Upload a pre-trained model as a Vertex AI Model resource.
- Create an Vertex AI Endpoint resource.
- Deploy the Model resource to the Endpoint resource.
- Configure the Endpoint resource for model monitoring.
- Upload a pre-trained model as a `Vertex AI Model` resource.
- Create an `Vertex AI Endpoint` resource.
- Deploy the `Model` resource to the `Endpoint` resource.
- Configure the `Endpoint` resource for model monitoring.
- Generate synthetic prediction requests.
- Understand how to interpret the statistics, visualizations, other data reported by the model monitoring feature.
</blockquote>
@@ -205,12 +205,12 @@ The steps performed include:
[Tracking hyperparameters and metrics in locally trained job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
<blockquote>
In this notebook, you learn how to use Vertex ML Metadata to track training parameters and evaluation metrics.
In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
This tutorial uses the following Google Cloud ML services:
- Vertex ML Metadata
- Vertex AI Experiments
- `Vertex ML Metadata`
- `Vertex AI Experiments`
The steps performed include:
@@ -223,11 +223,11 @@ The steps performed include:
[Creating Python function KFP components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
<blockquote>
In this tutorial, you learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use Vertex AI Pipelines to execute the pipeline.
In this tutorial, you learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline.
This tutorial uses the following Google Cloud ML services:
- Vertex AI Pipelines
- `Vertex AI Pipelines`
The steps performed include:
@@ -236,31 +236,31 @@ The steps performed include:
- Pass Artifacts and parameters between components, both by path reference and by value.
- Use the kfp.dsl.importer method.
- Compile the KFP pipeline.
- Execute the KFP pipeline using Vertex AI Pipelines
- Execute the KFP pipeline using `Vertex AI Pipelines`
</blockquote>
[AutoML image classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
<blockquote>
In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build an AutoML image classification model.
In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.
This tutorial uses the following Google Cloud ML services:
- Vertex AI Pipelines
- Google Cloud Pipeline Components
- Vertex AutoML
- Vertex AI Model resource
- Vertex AI Endpoint resource
- `Vertex AI Pipelines`
- `Google Cloud Pipeline Components`
- `Vertex AutoML`
- `Vertex AI Model` resource
- `Vertex AI Endpoint` resource
The steps performed include:
- Create a KFP pipeline:
- Create a Dataset resource.
- Train an AutoML image classification Model resource.
- Create an Endpoint resource.
- Deploys the Model resource to the Endpoint resource.
- Create a `Vertex AI Dataset` resource.
- Train an `AutoML` image classification `Model` resource.
- Create an `Endpoint` resource.
- Deploys the `Model` resource to the `Endpoint` resource.
- Compile the KFP pipeline.
- Execute the KFP pipeline using Vertex AI Pipelines
- Execute the KFP pipeline using `Vertex AI Pipelines`
</blockquote>
[AutoML tabular classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
@@ -357,6 +357,20 @@ The steps performed include:
[Introduction to KFP components and pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
<blockquote>
In this tutorial, you use the KFP SDK to build pipelines.\n",
This tutorial uses the following Google Cloud ML services:
- `Vertex AI Pipelines`
The steps performed include:
- Define and compile a `Vertex AI` pipeline.
- Schedule a recurring pipeline run.
- Specify which service account to use for a pipeline run.
</blockquote>
### Vertex AI Vizier
[Using Vizier for multi-objective study](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb)