- AutoML: Create and train models with minimal technical knowledge and effort. To learn more about AutoML, see AutoML beginner's guide.
- Custom training: Create a training application that's optimized for your targeted outcome.
For help on deciding which of these methods to use, see Choose a training method.
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.
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.|
|Text data||Classification, entity extraction, sentiment analysis.|
|Tabular data||Classification/regression, forecasting.|
The workflow for training and using an AutoML model is the same, regardless of your datatype 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.
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.
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.
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.
Vertex AI allows you to perform machine learning with tabular data using simple 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.
Additionally, 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.
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.
Classification for text
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.
Entity extraction for text
An entity extraction model inspects text data for known entities referenced in the data and labels those entities in the text.
Sentiment analysis for 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.
For a Jupyter Notebook that illustrates training a text classification model with AutoML, see:
|Vertex AI feature||Notebook||Description||Open in|
|AutoML||Text classification model||Create, train, and deploy a text classification model on Vertex AI.|
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.
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.
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.
Vertex AI allows you to train your custom training code using any machine learning framework on a variety of supported Compute Engine VMs with optional GPUs and TPUs. Get started by learning the requirements for your custom training code in Code requirements.