Train and use your own models (original) (raw)

Train and use your own models

Stay organized with collections Save and categorize content based on your preferences.

This page provides an overview of the workflow for training and using your own machine learning (ML) models on Vertex AI. Vertex AI offers the following methods for model training:

For help on deciding which of these methods to use, seeChoose 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, seeAutoML 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:

To learn more about custom training on Vertex AI, seeCustom 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 seeRay on Vertex AI overview.