Organiza tus páginas con colecciones
Guarda y categoriza el contenido según tus preferencias.
Patrones de referencia
En esta página, se proporcionan vínculos de casos de uso empresariales, códigos de muestra y guías de referencia técnica para casos de uso de BigQuery ML. Usa estos recursos para identificar prácticas recomendadas y acelerar el desarrollo de tu aplicación.
Regresión logística
En este patrón, se muestra cómo usar la regresión logística para realizar el modelado de propensión en aplicaciones de juegos.
Aprende a usar BigQuery ML para entrenar, evaluar y obtener predicciones de diversos tipos de modelos de propensión diferentes.
Los modelos de propensión pueden ayudarte a determinar la probabilidad de que usuarios específicos vuelvan a tu app para que puedas usar esa información en decisiones de marketing.
Prevé a partir de Hojas de cálculo mediante BigQuery ML
Si deseas aprender a poner en funcionamiento el aprendizaje automático con tus procesos empresariales, combina las Hojas conectadas con un modelo de previsión en BigQuery ML. En este patrón, se explica el proceso de compilación de un modelo de previsión para el tráfico del sitio web mediante los datos de Google Analytics. Puedes extender este patrón para que funcione con otros tipos de datos y otros modelos de aprendizaje automático.
En este patrón, se muestra cómo usar la detección de anomalías para encontrar fraudes con tarjetas de crédito en tiempo real.
Aprende cómo usar las transacciones y los datos de clientes para entrenar modelos de aprendizaje automático en BigQuery ML que se puedan usar en una canalización de datos en tiempo real para identificar, analizar y activar alertas para un posible fraude con tarjeta de crédito.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2025-09-04 (UTC)"],[[["\u003cp\u003eThis page provides resources such as business use cases, sample code, and technical guides for various BigQuery ML applications.\u003c/p\u003e\n"],["\u003cp\u003eLearn to create propensity models with logistic regression, which can determine the likelihood of user engagement, such as returning to your app.\u003c/p\u003e\n"],["\u003cp\u003eExplore time-series forecasting patterns to build models for predicting retail demand for products.\u003c/p\u003e\n"],["\u003cp\u003eDiscover how to combine Connected Sheets with a forecasting model in BigQuery ML to operationalize machine learning for business tasks, like forecasting website traffic.\u003c/p\u003e\n"],["\u003cp\u003eUtilize anomaly detection patterns to identify and analyze potential credit card fraud in real-time using machine learning models trained in BigQuery ML.\u003c/p\u003e\n"]]],[],null,["# Reference patterns\n==================\n\nThis page provides links to business use cases, sample code, and technical\nreference guides for BigQuery ML use cases. Use these resources to\nidentify best practices and speed up your application development.\n\nLogistic regression\n-------------------\n\nThis pattern shows how to use logistic regression to perform propensity\nmodeling for gaming applications.\n\nLearn how to use BigQuery ML to train, evaluate, and get\npredictions from several different types of propensity models.\nPropensity models can help you to determine the likelihood of specific\nusers returning to your app, so you can use that information in\nmarketing decisions.\n\n- Blog post: [Churn prediction for game developers using Google Analytics 4 and BigQuery ML](/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml)\n- Notebook: [Churn prediction solution notebook](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/gaming/propensity-model/bqml)\n\nTime-series forecasting\n-----------------------\n\nThese patterns show how to create time-series forecasting solutions.\n\n### Build a demand forecasting model\n\nLearn how to build a time series model that you can use to forecast retail\ndemand for multiple products.\n\n- Blog post: [How to build demand forecasting models with BigQuery ML](/blog/topics/developers-practitioners/how-build-demand-forecasting-models-bigquery-ml)\n- Notebook: [Demand forecasting solution notebook](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/time-series/bqml-demand-forecasting/bqml_retail_demand_forecasting.ipynb)\n\n### Forecast from Google Sheets using BigQuery ML\n\nLearn how to operationalize machine learning with your business\nprocesses by combining\n[Connected Sheets](/bigquery/docs/connected-sheets) with a forecasting\nmodel in BigQuery ML. This pattern walks you through\nthe process for building a forecasting model for website traffic using\nGoogle Analytics data. You can extend this pattern to work\nwith other data types and other machine learning models.\n\n- Blog post: [How to use a machine learning model from Google Sheets using BigQuery ML](/blog/topics/developers-practitioners/how-use-machine-learning-model-google-sheet-using-bigquery-ml)\n- Sample code: [BigQuery ML forecasting with Sheets](https://github.com/googleworkspace/ml-integration-samples/tree/master/apps-script/BQMLForecasting)\n- Template: [BigQuery ML forecasting with Sheets](https://docs.google.com/spreadsheets/d/1njedwGjBOkUbTS_HYD0wIPuQIDHgobp1D80qO-OsNH0/copy)\n\nAnomaly detection\n-----------------\n\nThis pattern shows how to use anomaly detection to find real-time credit\ncard fraud.\n\nLearn how to use transactions and customer data to train machine\nlearning models in BigQuery ML that can be used in a\nreal-time data pipeline to identify, analyze, and trigger alerts for\npotential credit card fraud.\n\n- Sample code: [Real-time credit card fraud detection](https://github.com/googlecloudplatform/fraudfinder)\n- Overview video: [Fraudfinder: A comprehensive solution for real data science problems](https://io.google/2022/program/9a759b60-9a9b-4744-bd22-6e21a4a864cd/)"]]
