[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb)
Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.
The steps performed include:
- Train an AutoML image classification model.
- Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction
Learn more about Migrate to Vertex AI.
Learn more about Classification for image data.
[Custom Scikit-Learn model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb)
Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
The steps performed include:
- Create a `Vertex AI` custom job for training a scikit-learn model.
- Upload the trained model artifacts as a `Model` resource.
- Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction
Learn more about Migrate to Vertex AI.
Learn more about Custom training.
[Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb)
Learn to use `Vertex AI Hyperparameter` to create and tune a custom trained model.
The steps performed include:
- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model.
Learn more about Migrate to Vertex AI.
Learn more about Custom training.
[AutoML Video Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
The steps performed include:
- Train an AutoML video classification model.
- Make a batch prediction.
Learn more about Migrate to Vertex AI.
Learn more about Classification for video data.
[AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb)
Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
The steps performed include:
- Train an AutoML video object tracking model.
- Make a batch prediction.
Learn more about Migrate to Vertex AI.
Learn more about Object tracking for video data.
[Custom Image Classification w/pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb)
Learn how to train a tensorflow image classification model using a prebuilt container and Vertex AI training.
The steps performed include:
- *Package the training code into a python application.*
- *Containerize the training application using Cloud Build and Artifact Registry.*
- *Create a custom container training job in Vertex AI and run it.*
- *Evaluate the model generated from the training job.*
- *Create a model resource for the trained model in Vertex AI Model Registry.*
- *Run a Vertex AI batch prediction job.*
- *Deploy the model resource to a Vertex AI Endpoint.*
- *Run a online prediction job on the model resource.*
- *Clean up the resources created.*
Learn more about Migrate to Vertex AI.
Learn more about Custom training.
[Custom Image Classification w/custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb)
Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
The steps performed include:
- *Package the training code into a python application.*
- *Containerize the training application using Cloud Build and Artifact Registry.*
- *Create a custom container training job in Vertex AI and run it.*
- *Evaluate the model generated from the training job.*
- *Create a model resource for the trained model in Vertex AI Model Registry.*
- *Run a Vertex AI batch prediction job.*
- *Deploy the model resource to a Vertex AI Endpoint.*
- *Run a online prediction job on the model resource.*
- *Clean up the resources created.*
Learn more about Migrate to Vertex AI.
Learn more about Custom training.
[AutoML Tabular Binary Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb)
In this tutorial, you create an AutoML tabular binary classification 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 Migrate to Vertex AI.
Learn more about Classification for tabular data.
[AutoML Image Object Detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb)
Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.
The steps performed include:
- Train an AutoML object detection model.
- Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction
Learn more about Migrate to Vertex AI.
Learn more about Object detection for image data.
[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb)
The objective of this notebook is to build a AutoML Text Classification Model.
The steps performed include the following:
* Set your task name, and GCS prefix
* Copy AutoML text demo train data for creating managed dataset
* Create a dataset on Vertex AI.
* Configure a training job
* Launch a training job and create a model on Vertex AI
* Copy AutoML Text Demo Prediction Data for creating batch prediction job
* Perform batch prediction job on the model
Learn more about Migrate to Vertex AI.
Learn more about Classification for text data.
[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
The objective of this notebook is to build a AutoML Text Entity Extraction model.
The steps performed include the following:
* Set your task name, and GCS prefix
* Copy AutoML video demo train data for creating managed dataset
* Create a dataset on Vertex AI.
* Configure a training job
* Launch a training job and create a model on Vertex AI
* Copy AutoML Video Demo Prediction Data for creating batch prediction job
* Perform batch prediction job on the model
Learn more about Migrate to Vertex AI.
Learn more about Entity extraction for text data.
[AutoML Text Sentiment Analysis](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb)
The objective of this notebook is to build a AutoML Text Sentiment Analysis model.
The steps performed include the following:
* Copy AutoML video demo train data for creating managed dataset
* Create a dataset on Vertex AI.
* Configure a training job
* Launch a training job and create a model on Vertex AI
* Copy AutoML Video Demo Prediction Data for creating batch prediction job
* Perform batch prediction job on the model
Learn more about Migrate to Vertex AI.
Learn more about Sentiment analysis for text data.
[Custom XGBoost model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb)
Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
The steps performed include:
- Create a `Vertex AI` custom job for training a scikit-learn model.
- Upload the trained model artifacts as a `Model` resource.
- Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction
Learn more about Migrate to Vertex AI.
Learn more about Custom training.