This page provides an overview of the workflow for training and using your own
models on Vertex AI. Vertex AI offers two methods for model
training:
- AutoML
: Create and train models with minimal technical knowledge
and effort. To learn more about AutoML, see
AutoML beginner's guide
.
- 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
.
For help on deciding which of these methods to use, see
Choose a training method
.
AutoML
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
lets you build a code-free model based on the training data that 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.
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 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
.
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
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 the authors.
Vertex AI lets you get online predictions and batch predictions from
your text-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 text 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 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.
Documentation:
Prepare data
|
Create dataset
|
Train model
|
Evaluate model
|
Get predictions
|
Interpret results
An
entity extraction
model inspects text data for known entities referenced
in the data and labels those entities in the text.
Documentation:
Prepare data
|
Create dataset
|
Train model
|
Evaluate model
|
Get predictions
|
Interpret results
Sentiment analysis for text
A
sentiment analysis
model inspects text data and identifies the prevailing
emotional state within it, especially to determine a writer's attitude as
positive, negative, or neutral.
Documentation:
Prepare data
|
Create dataset
|
Train model
|
Evaluate model
|
Get predictions
|
Interpret results
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
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
.