- AutoML: Create and train models with minimal technical knowledge and effort. To learn more about AutoML, see AutoML beginner's guide.
- Vertex AI custom training: Create and train models at scale using any ML framework. To learn more about custom training on Vertex AI, see Custom training overview.
- Ray on Vertex AI: Use open source Ray code to write programs and develop applications on Vertex AI with minimal changes.
For help on deciding which of these methods to use, see Choose a training method.
AutoML
AutoML on Vertex AI lets you build a code-free ML model based on the training data that you provide. AutoML can automate tasks like data preparation, model selection, hyperparameter tuning, and deployment for various data types and prediction tasks, which can make ML more accessible for a wide range of users.
Types of models you can build using AutoML
The types of models you can build depend on the type of data that you have. Vertex AI offers AutoML solutions for the following data types and model objectives:
| Data type | Supported objectives |
|---|---|
| Image data | Classification, object detection. |
| Video data | Action recognition, classification, object tracking. |
| Tabular data | Classification/regression, forecasting. |
To learn more about AutoML, see AutoML training overview.
Vertex AI custom training
If none of the AutoML solutions address your needs, you can also create your own training application and use it to train custom models on Vertex AI. You can use any ML framework that you want and configure the compute resources to use for training, including the following:
- Type and number of VMs.
- Graphics processing units (GPUs).
- Tensor processing units (TPUs).
- Type and size of boot disk.
To learn more about custom training on Vertex AI, see Custom training overview.
Ray on Vertex AI
Ray on Vertex AI is a service that lets you use the open-source Ray framework for scaling AI and Python applications directly within the Vertex AI platform. Ray is designed to provide the infrastructure for distributed computing and parallel processing for your ML workflow.
Ray on Vertex AI provides a managed environment for running distributed applications using the Ray framework, offering scalability and integration with Google Cloud services.
To learn more about Ray on Vertex AI see Ray on Vertex AI overview.