Predictive analytics previously required complex custom model building and training cycles. This traditional approach involved manual feature engineering, lengthy tuning, and specialized skills, often leading to underutilization of predictive models. Google Research has introduced TabFM, a pre-trained foundation model for tabular data, now available in BigQuery. TabFM utilizes in-context learning to deliver instant, highly accurate predictions via a single SQL statement, eliminating separate training and deployment steps. This system offers zero-shot predictions, simplifying the process for users without extensive machine learning expertise. It also enhances agentic applications by providing predictive powers without managing infrastructure. TabFM demonstrates state-of-the-art accuracy, outperforming traditional models on benchmarks. The developer experience is simplified through native BigQuery integration and straightforward SQL syntax. TabFM automatically handles feature engineering tasks, and its scalability allows processing millions of rows quickly. Users can leverage AI.PREDICT for instant predictions and AI.EVALUATE to assess model performance. While TabFM excels in rapid insights and frequent retraining, traditional models remain suitable for very large datasets or when feature importance explainability is crucial. This integration makes predictive machine learning as easy as running a standard SQL query.