Machine Learning and Inference in BigQuery using SQL

Data analysts often face roadblocks when moving data out of databases to build and run machine learning models. BigQuery Machine Learning (BQML) solves this by letting you train and execute predictive models directly where your data lives, using standard SQL. This course helps you transition from a traditional data analyst to a predictive analyst. You will learn how to write SQL queries that build, evaluate, and deploy machine learning models for classification, regression, and forecasting, as well as integrate modern generative AI model inferences. What you will learn: 1. Understand foundational machine learning concepts and core predictive modeling vocabulary. 2. Create and train supervised learning models directly inside BigQuery using standard SQL syntax. 3. Evaluate model performance using key metrics like precision, recall, and mean absolute error. 4. Generate real-time predictions and batch inferences on large datasets without exporting data. 5. Configure modern integrations such as remote models and large language model inferences. 6. Apply best practices for structuring data and managing machine learning workflows. This text-based guide starts with fundamental ML terminology before moving step-by-step through practical SQL-based modeling scenarios. You will read clear explanations, analyze query structures, and practice writing your own ML statements. Designed for data analysts and SQL users who want to add machine learning to their toolkit, there are no prior programming prerequisites. Start learning today and unlock the predictive power of your SQL database.