Tabular data overview

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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.

For an introduction to machine learning with tabular data, see: Introduction to Tabular Data.

A note about fairness

Google is committed to making progress in following responsible AI practices. To this end, our ML products, including AutoML, are designed around core principles such as fairness and human-centered machine learning. For more information about best practices for mitigating bias when building your own ML system, see Inclusive ML guide - AutoML.

Vertex AI solutions

Classification and regression with AutoML

Classification and regression with AutoML is an integrated, fully managed pipeline for end-to-end ML. Vertex AI searches for the optimal set of hyperparameters, trains multiple models with multiple sets of hyperparameters and then creates a single, final model from an ensemble of the top models. Vertex AI considers neural networks and boosted trees for the model types.

Benefits

  • Easy to use: model type, model parameters, and hardware are chosen for you.

For further information, see: Classification and Regression Overview.

Forecasting with AutoML or Seq2Seq+

Forecasting with AutoML or Seq2Seq+ are integrated, fully managed pipelines for end-to-end ML. Vertex AI searches for the optimal set of hyperparameters, trains multiple models with multiple sets of hyperparameters, and then creates a single, final model from an ensemble of the top models. Vertex AI considers only neural networks for the model type.

Benefits

  • Easy to use: model type, model parameters, and hardware are chosen for you.

For further information, see: Forecasting Overview.

Forecasting with BigQuery ML ARIMA_PLUS

Forecasting with BigQuery ML ARIMA_PLUS is an integrated, fully managed pipeline for end-to-end ML.

Benefits

  • Easy to use: model type, model parameters, and hardware are chosen for you.
  • Fast: model training gives a cheap baseline to compare other models against.

For further information, see: Forecasting with ARIMA+

Tabular Workflows

Tabular Workflows is a set of integrated, fully managed, and scalable pipelines for end-to-end ML with tabular data. It leverages Google's technology for model development and provides you with customization options to fit your needs.

Benefits

  • Fully managed: you don't need to worry about updates, dependencies and conflicts.
  • Easy to scale: you don't need to re-engineer infrastructure as workloads or datasets grow.
  • Optimized for performance: the right hardware is automatically configured for the workflow's requirements.
  • Deeply integrated: compatibility with products in the Vertex AI MLOps suite, like Vertex AI Pipelines and Vertex AI Experiments, allows you to run many experiments in a short amount of time.

For further information, see: Tabular Workflows on Vertex AI.

What's next