Batch Churn Predictions with ML.PREDICT

What ML.PREDICT is:

ML.PREDICT is the standard BigQuery ML function used to generate predictions, classifications, or cluster assignments using a previously trained machine learning model. It allows data practitioners to run batch inference directly inside BigQuery using standard SQL syntax without exporting data or managing external prediction microservices.

How it meets the requirements:

• Batch inference execution (✓): ML.PREDICT takes the trained model name (customer_churn_model) and an input table or subquery (active_customers) to generate predicted labels for each record in parallel.
• Direct SQL integration (✓): The output of ML.PREDICT includes all original input columns alongside new prediction columns (such as predicted_ and predicted__probs), making it easy to filter, aggregate, or join directly with downstream marketing and reporting tables.
• Serverless scaling (✓): It leverages BigQuery's distributed compute engine to perform predictions over millions of records in seconds without provisioning infrastructure.

Key advantages and capabilities:

• Seamless integration: Operates within standard SQL queries, enabling predictions to be written directly to new tables or visualized in Looker.
• Preserves input columns: Automatically passes through all feature columns and identifying keys (like customer_id) from the input data alongside prediction outputs.
• Probability outputs: For classification models, it returns prediction probabilities for each potential class, enabling granular thresholding and targeting.
• Broad model support: Works across multiple model types, including logistic regression, linear regression, boosted trees, random forests, deep neural networks (DNNs), and imported TensorFlow or ONNX models.

Why this is optimal:

ML.PREDICT is specifically designed for supervised and unsupervised batch inference on tabular datasets. It minimizes operational complexity and latency by executing predictive scoring directly where the data resides.