AutoML model types

Machine learning (ML) models use training data to learn how to infer results for data that the model was not trained on. AutoML on Vertex AI enables you to build a code-free model based on the training data you provide.

This document describes some of the types of problems that you can solve using AutoML.

Types of models you can build using AutoML

The types of models you can build depend on the type of data that you have. The following sections describe the types of models that you can build with image data, tabular data, text data, and video data.

To solve a complex problem that doesn't match the problem types in the following sections, look for ways to split your problem into a set of smaller problems. Combining multiple models may help you solve an especially complex problem.

Image data

AutoML uses machine learning to analyze the content of image data. You can use AutoML to train an ML model to classify image data or find objects in image data.

  • A classification model analyzes image data and returns a list of content categories that apply to the image. For example, you could train a model that classifies images as containing a cat or not containing a cat, or you could train a model to classify images of dogs by breed.

  • An object detection model analyzes your image data and returns annotations for all objects found in an image, consisting of a label and bounding box location for each object. For example, you could train a model to find the location of the cats in image data.

To start building your image data model using AutoML:

Tabular data

AutoML uses machine learning to analyze the content of tabular data. You can use AutoML to train an ML model to use regression to find a numeric value, or use classification to predict a categorical outcome from your tabular data.

  • A regression model analyzes your tabular data and returns a numeric value. For example, you could train a model to estimate the value of a house.

  • A classification model analyzes your tabular data and returns a list of categories that describe the data. For example, you could train a model to predict whether the purchase history for a customer predicts that they will buy a subscription or not.

  • A forecasting model (Preview) uses multiple rows of time-dependent tabular data from the past to predict a series of numeric values that extend into the future. For example, by forecasting future product demand, a retail organization could optimize its supply chain to reduce the chance of overstocking or selling out of that product.

To start building your tabular data model using AutoML:

Text data

AutoML uses machine learning to analyze the structure and meaning of text data. You can use AutoML to train an ML model to classify text data, extract information, or understand the sentiment of authors.

  • A classification model analyzes text data and returns a list of categories that apply to the text found in the data. Vertex AI offers both single-label and multi-label text classification models.

  • An entity extraction model inspects text data for known entities referenced in the data and labels those entities in the text.

  • A sentiment analysis model inspects text data and identifies the prevailing emotional opinion within it, especially to determine a writer's attitude as positive, negative, or neutral.

To start building your text data model using AutoML:

Video data

AutoML uses machine learning to analyze video data to classify shots and segments, or to detect and track multiple objects in your video data.

  • A classification model analyzes your video data and returns a list of categorized shots and segments. For example, you could train a model that analyzes video data to identify if the video is of a soccer, baseball, basketball, or football game.

  • An object tracking model analyzes your video data and returns a list of shots and segments where these objects were detected. For example, you could train a model that analyzes video data from soccer games to identify and track the ball.

  • An action recognition model analyzes your video data and returns a list of categorized actions with the moments the actions happened. For example, you could train a model that analyzes video data to identify the action moments involving a soccer goal, a golf swing, a touchdown, or a high five.

To start building your video data model using AutoML: