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. |
The workflow for training and using an AutoML model is the same, regardless of your data type or objective:
- Prepare your training data.
- Create a dataset.
- Train a model.
- Evaluate and iterate on your model.
- Get predictions from your model.
- Interpret prediction results.
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.
Vertex AI lets you get online predictions and batch predictions from your image-based models. Online predictions are synchronous requests made to a model endpoint. Use online predictions when you are making requests in response to application input or in situations that require timely inferences. Batch predictions are asynchronous requests. You request batch predictions directly from the model resource without needing to deploy the model to an endpoint. For image data, use batch predictions when you don't require an immediate response and want to process accumulated data by using a single request.
Classification for images
A classification model analyzes image data and returns a list of content categories that apply to the image. For example, you can 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.
Documentation: Prepare data | Create dataset | Train model | Evaluate model | Get predictions | Interpret results
Object detection for images
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 can train a model to find the location of the cats in image data.
Documentation: Prepare data | Create dataset | Train model | Evaluate model | Get predictions | Interpret results
Tabular data
Vertex AI lets you perform machine learning with tabular data using straightforward processes and interfaces. You can create the following model types for your tabular data problems:
- Binary classification models predict a binary outcome (one of two classes). Use this model type for yes or no questions. For example, you might want to build a binary classification model to predict whether a customer would buy a subscription. Generally, a binary classification problem requires less data than other model types.
- Multi-class classification models predict one class from three or more discrete classes. Use this model type for categorization. For example, as a retailer, you might want to build a multi-class classification model to segment customers into different personas.
- Regression models predict a continuous value. For example, as a retailer, you might want to build a regression model to predict how much a customer will spend next month.
- Forecasting models predict a sequence of values. For example, as a retailer, you might want to forecast daily demand of your products for the next 3 months so that you can appropriately stock product inventories in advance.
To learn more, see Tabular data overview.
If your tabular data is stored in BigQuery ML, you can train an AutoML tabular model directly in BigQuery ML. To learn more, see AutoML Tabular reference documentation.
Text data
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.
Action recognition for videos
An action recognition model analyzes your video data and returns a list of categorized actions with the moments that the actions happened. For example, you can 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.
Documentation: Prepare data | Create dataset | Train model | Evaluate model | Get predictions | Interpret results
Classification for videos
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 baseball, soccer, basketball, or football game.
Documentation: Prepare data | Create dataset | Train model | Evaluate model | Get predictions | Interpret results
Object tracking for videos
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.
Documentation: Prepare data | Create dataset | Train model | Evaluate model | Get predictions | Interpret results