The Future of DAX: Integrating Python ML Models into Power BI

Abstract

Despite rapid advancements in business intelligence (BI) platforms and machine learning (ML), the integration of DAX (Data Analysis Expressions), Python-based ML models, and Power BI remains fragmented, both academically and industrially. Current implementations that involve machine learning in Power BI are often restricted to either isolated Power Query steps or static Python visuals, which do not support real-time interactivity or native integration within the data model layer. Moreover, the absence of a dedicated function within the DAX language to invoke machine learning predictions constrains the analytical depth and agility of BI solutions.
This research focuses on addressing these limitations by proposing the design and implementation of a native PREDICT() DAX function. This function would enable Power BI users to load and score ONNX (Open Neural Network Exchange) formatted machine learning models directly within DAX measures and calculated columns. The PREDICT() function is envisioned as a lightweight, efficient, and secure alternative to external ML services, supporting real-time scoring and seamless interaction with report filters and slicers. By embedding ML logic directly into the semantic model, this approach allows business users to derive predictive insights interactively, without external dependencies or complex refresh cycles.
The paper presents a detailed architecture for the proposed function, including its integration within the VertiPaq engine, data flow between the semantic model and ONNX runtime, and sandboxing for security compliance. It also outlines use cases such as demand forecasting, churn prediction, and anomaly detection, demonstrating how native ML scoring in DAX can transform business decision-making. By reducing technical barriers and centralizing analytics within the BI tool, the proposed approach aims to democratize access to predictive modeling and elevate the strategic impact of Power BI across organizations.