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Autoindex official (#1424)
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This commit is contained in:
Andrew Ferlitsch
@@ -12,6 +12,8 @@ The steps performed include:
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```
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Learn more about [Vertex AI Model Monitoring for batch predictions](https://cloud.google.com/vertex-ai/docs/model-monitoring/model-monitoring-batch-predictions).
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[Vertex AI Model Monitoring with Explainable AI Feature Attributions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
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@@ -30,3 +32,5 @@ The steps performed include:
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```
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Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
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@@ -61,7 +61,9 @@
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"source": [
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"## Overview\n",
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"\n",
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"In this notebook, you will learn how to use Model Monitoring with batch prediction requests on a deployed Vertex AI Model resource. In a companion notebook, <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb\" target=\"_blank\">Vertex AI Model Monitoring with Explainable AI Feature Attributions</a>, you can learn about how to apply model monitoring to streaming, real-time predictions."
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"In this notebook, you will learn how to use Model Monitoring with batch prediction requests on a deployed Vertex AI Model resource. In a companion notebook, <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb\" target=\"_blank\">Vertex AI Model Monitoring with Explainable AI Feature Attributions</a>, you can learn about how to apply model monitoring to streaming, real-time predictions.\n",
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"\n",
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"Learn more about [Vertex AI Model Monitoring for batch predictions](https://cloud.google.com/vertex-ai/docs/model-monitoring/model-monitoring-batch-predictions)."
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]
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},
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{
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@@ -80,7 +80,9 @@
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"\n",
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"[Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview) adds another facet to model monitoring, which we call feature attribution monitoring. Explainable AI enables you to understand the relative contribution of each feature to a resulting prediction. In essence, it assesses the magnitude of each feature's influence.\n",
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"\n",
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"If production traffic differs from training data, or varies substantially over time, **either in terms of model predictions or feature attributions**, that's likely to impact the quality of the answers your model produces. When that happens, you'd like to be alerted automatically and responsively, so that **you can anticipate problems before they affect your customer experiences or your revenue streams**."
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"If production traffic differs from training data, or varies substantially over time, **either in terms of model predictions or feature attributions**, that's likely to impact the quality of the answers your model produces. When that happens, you'd like to be alerted automatically and responsively, so that **you can anticipate problems before they affect your customer experiences or your revenue streams**.\n",
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"\n",
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"Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring)."
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]
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},
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{
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@@ -1,7 +1,4 @@
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### pipelines
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[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
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```
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@@ -19,6 +16,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
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[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)
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@@ -36,6 +37,8 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
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@@ -53,6 +56,8 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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[Custom training with pre-built Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)
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@@ -70,6 +75,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline).
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[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
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@@ -88,6 +97,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
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[Training an acquisition-prediction model using Swivel, BigQuery ML and Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb)
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@@ -105,6 +118,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).
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[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
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@@ -118,6 +135,8 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
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@@ -136,6 +155,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
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[Model upload, predict, and evaluate using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb)
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@@ -151,6 +174,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [Vertex AI Model components](https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component).
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[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
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@@ -166,22 +193,28 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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[Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb)
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```
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Learn how to build a Vertex AI pipeline and train a Random-forest model using Spark ML for loan-eligibility classification problem.
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Learn how to build a Vertex AI pipeline and train a random-forest model using Spark ML for loan-eligibility classification problem.
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The steps performed include:
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* Use the `DataprocPySparkBatchOp` to preprocess data.
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* Create a Vertex AI dataset resource on the training data.
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* Train a random forest model using Pyspark.
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* Train a random forest model using PySpark.
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* Build a Vertex AI pipeline and run the training job.
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* Use the Spark serving image in order to deploy a Spark model on Vertex AI Endpoint.
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [Dataproc components](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
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[Model train, upload, and deploy using Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb)
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@@ -200,6 +233,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/pipelines/customjob-component).
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[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
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@@ -218,6 +255,10 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
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[Training and batch prediction with BigQuery source and destinantion for a custom tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb)
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@@ -240,3 +281,7 @@ The steps performed include:
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```
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Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
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Learn more about [Vertex AI Batch Prediction components](https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component).
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@@ -1,7 +1,4 @@
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### prediction
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[Custom model batch prediction with feature filtering](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/custom_batch_prediction_feature_filter.ipynb)
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```
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@@ -15,3 +12,5 @@ The steps performed include:
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```
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Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
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