Deploying Iris-detection model using FastAPI and Vertex AI custom container serving
Learn how to create, deploy and serve a custom classification model on Vertex AI.
The steps performed include:
- Train a model that uses flower's measurements as input to predict the class of iris.
- Save the model and its serialized pre-processor.
- Build a FastAPI server to handle predictions and health checks.
- Build a custom container with model artifacts.
- Upload and deploy custom container to Vertex AI Endpoints.
Learn more about Custom training.
Learn more about Vertex AI Prediction.
Training and deploying a sales forecasting model using FBProphet and Vertex AI
The objective of this notebook is to create, deploy and serve a custom forecasting model on Vertex AI.
The steps performed include:
- Train a model locally that forecasts sales for the given number of days.
- Train another model that uses both sales and weather data for sales prediction.
- Save both the models.
- Build a FastAPI server to handle the predictions for the chosen model.
- Build a custom container image of the serving application with the model artifacts.
- Upload the model to Vertex AI Model Registry.
- Deploy the model to a Vertex AI Endpoint.
- Send online prediction requests to the deployed model.
- Clean up the resources created in this session.
Learn more about Custom training.
Learn more about Vertex AI Prediction.
Training a TensorFlow model on BigQuery data
Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data.
The steps performed include:
- Create a Vertex AI custom `TrainingPipeline` for training a model.
- Train a TensorFlow model.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model` resource.
Learn more about Vertex AI Training.
Custom training with custom training container and automatic registering of the model
In this tutorial, you create a custom model from a Python script in a custom Docker container using the Vertex AI SDK, and automatically register the model in the Vertex AI Model Registry.
The steps performed include:
- Create a Vertex AI custom job for training a model.
- Train and register a TensorFlow model using a custom container,
- List the registered model from the Vertex AI Model Registry.
Learn more about Custom training.
Profile model training performance using Profiler
Learn how to enable Vertex AI TensorBoard Profiler for custom training jobs.
The steps performed include:
- Setup a service account and a Cloud Storage bucket
- Create a TensorBoard instance
- Create and run a custom training job
- View the TensorBoard Profiler dashboard
Learn more about Vertex AI TensorBoard Profiler.
Get started with Vertex AI Training for XGBoost
Learn how to use `Vertex AI Training` for training a XGBoost custom model.
The steps performed include:
- Training using a Python package.
- Report accuracy when hyperparameter tuning.
- Save the model artifacts to Cloud Storage using GCSFuse.
- Create a `Vertex AI Model` resource.
Learn more about Custom training.
Get started with Endpoint and shared VM
Learn how to use deployment resource pools for deploying models.
The steps performed include:
- Upload a pre-trained image classification model as a `Model` resource (model A).
- Upload a pre-trained text sentence encoder model as a `Model` resource (model B).
- Create a shared VM deployment resource pool.
- List shared VM deployment resource pools.
- Create two `Endpoint` resources.
- Deploy first model (model A) to first `Endpoint` resource using deployment resource pool.
- Deploy second model (model B) to second `Endpoint` resource using deployment resource pool.
- Make a prediction request with first deployed model (model A).
- Make a prediction request with second deployed model (model B).
Learn more about Shared resources across deployments.
Custom training and batch prediction
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 TensorFlow model.
- Upload the trained model artifacts as a `Model` resource.
- Make a batch prediction.
Learn more about Custom training.
Learn more about Vertex AI Batch Prediction.
Custom training and online prediction
Learn to use `Vertex AI Training` to create a custom-trained model from a Python script in a Docker container, and learn to use `Vertex AI Prediction` to do a prediction on the deployed model by sending data.
The steps performed include:
- Create a `Vertex AI` custom job for training a TensorFlow model.
- Upload the trained model artifacts to a `Model` resource.
- Create a serving `Endpoint` resource.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model` resource.
Learn more about Custom training.
Learn more about Vertex AI Prediction.