menu
arrow_back

Building Demand Forecasting with BigQuery ML

—/100

Checkpoints

arrow_forward

Explore the NYC Citi Bike Trips dataset

Cleaned training data

Training a Model

Evaluate the time series model

Make Predictions using the model

Building Demand Forecasting with BigQuery ML

1 hora 5 créditos

GSP852

Google Cloud Self-Paced Labs

Overview

BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage, or needing a database administrator.

BigQuery Machine Learning (BQML) is a feature in BigQuery where data analysts can create, train, evaluate, and predict with machine learning models with minimal coding. Watch this video to learn more about BigQuery ML.

In this lab, you will learn how to build a time series model to forecast the demand of multiple products using BigQuery ML. Using the NYC Citi Bike Trips public dataset, learn how to use historical data to forecast demand in the next 30 days. Imagine the bikes are retail items for sale, and the bike stations are stores.

Watch this video to understand some example use cases for demand forecasting.

Objectives

In this lab, you will learn to perform the following tasks:

  • Use BigQuery to find public datasets.

  • Query and explore the public NYC Citi Bike Trips dataset.

  • Create a training and evaluation dataset to be used for batch prediction.

  • Create a forecasting (time series) model in BQML.

  • Evaluate the performance of your machine learning model.

Únase a Qwiklabs para leer este lab completo… y mucho más.

  • Obtenga acceso temporal a Google Cloud Console.
  • Más de 200 labs para principiantes y niveles avanzados.
  • El contenido se presenta de a poco para que pueda aprender a su propio ritmo.
Únase para comenzar este lab