Have you ever wanted to build machine learning models without leaving your data warehouse? With BigQuery ML (BQML), you can create, train, and deploy machine learning models directly using SQL — no need to export data or become a Python expert.

In this article, we’ll explore what BigQuery ML is, why it matters, and walk through a practical example so you can get started right away.

🔍 What is BigQuery ML?

BigQuery ML is a feature in Google BigQuery that allows users to build and operationalize machine learning models using standard SQL queries. It brings the power of machine learning closer to analysts and BI professionals by enabling model training and prediction directly inside BigQuery.

⚡ Why Use BigQuery ML?

  • No data movement: Models are built and run on your existing BigQuery tables.
  • Scalability: Built on top of Google’s infrastructure.
  • Familiar syntax: If you know SQL, you can start using ML.
  • Integration: Works seamlessly with Looker, Data Studio, and other GCP tools.

🧠 What Kind of Models Can You Build?

BigQuery ML supports several model types, including:

Model Type Use Case
linear_reg Predicting numeric values (e.g. prices)
logistic_reg Binary classification (e.g. churn)
kmeans Clustering
time_series Forecasting
xgboost, dnn Advanced classification & regression

🧪 Hands-on Example: Predicting Customer Churn

Let’s build a logistic regression model to predict customer churn using public data.

📦 Step 1: Prepare the Dataset

We’ll use a mock dataset of customers with basic features like age, tenure, and contract type.

-- Preview the data
SELECT * FROM `your_project.your_dataset.customers` LIMIT 10;

🏗️ Step 2: Create the Model

We’ll split the data into training and evaluation using a column called data_split.

CREATE OR REPLACE MODEL `your_project.your_dataset.churn_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
  age,
  tenure,
  contract_type,
  monthly_charges,
  churned AS label
FROM
  `your_project.your_dataset.customers`
WHERE data_split = 'train';

📊 Step 3: Evaluate the Model

Use built-in evaluation metrics to understand the model’s performance.

SELECT *
FROM ML.EVALUATE(MODEL `your_project.your_dataset.churn_model`,
                 (SELECT
                    age,
                    tenure,
                    contract_type,
                    monthly_charges,
                    churned AS label
                  FROM `your_project.your_dataset.customers`
                  WHERE data_split = 'eval'));

🔮 Step 4: Make Predictions

Now use the model to predict the likelihood of churn for new users.

SELECT
  customer_id,
  predicted_label,
  predicted_probability
FROM ML.PREDICT(MODEL `your_project.your_dataset.churn_model`,
                (SELECT
                   customer_id,
                   age,
                   tenure,
                   contract_type,
                   monthly_charges
                 FROM `your_project.your_dataset.customers`
                 WHERE data_split = 'test'));

💡 Tips and Best Practices

  • Clean and normalize your data beforehand.
  • Use ML.FEATURE_INFO() to explore feature importance.
  • Try other model types like boosted_tree_classifier for more complex tasks.
  • Store model metadata in a tracking table.

🧽 Conclusion

BigQuery ML opens the doors to machine learning for data analysts. By leveraging SQL and your existing BigQuery environment, you can explore predictive analytics faster and more efficiently than ever before.

So why wait? Start experimenting today and turn your data into actionable insights with just a few lines of SQL.