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.
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.
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.
AutoML tabular binary classification
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.
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.
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
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.
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.
Custom image classification with a custom training container
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.
Custom image classification with a pre-built training container
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 Scikit-Learn model with pre-built training container
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.
Custom XGBoost model with pre-built training container
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.
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.