Batch Predictions with Deployed Models in Fabric

How do you apply your trained machine learning models to massive, real-world datasets? Generating batch predictions efficiently is a core requirement for modern data engineering and data science workflows.\n\nThis course guides you through the process of loading deployed machine learning models and running batch inference within the Fabric ecosystem. You will transition from understanding basic predictive workflows to setting up automated, high-performance prediction pipelines that handle large-scale data with ease.\n\nWhat you'll learn:\n- Understand the core concepts of batch prediction and model deployment in Fabric\n- Load registered machine learning models from the workspace registry\n- Apply models to large datasets using scalable Spark and Python workflows\n- Optimize inference performance with modern dataframe libraries and parallel processing\n- Write prediction outputs back to lakehouses or data warehouses for downstream consumption\n- Implement basic MLOps monitoring practices to track model prediction drift over time\n\nYou will begin by learning foundational machine learning deployment terminology and the Fabric architecture. From there, you will progress through step-by-step written explanations and Python code snippets that demonstrate how to load models, prepare input data, and store prediction results securely.\n\nThis course is designed for beginner data analysts, aspiring data scientists, and data engineers who want to operationalize machine learning models. No prior experience with Fabric is required, though a basic familiarity with Python is helpful.\n\nStart learning today and unlock the power of automated batch predictions in your data pipelines.