Fabric Data Science

Orchestrate reproducible ML workflows in Microsoft Fabric by loading data into notebooks, running experiments with MLflow, and registering versioned models. This satisfies governance and reproducibility requirements for production ML pipelines.

Yes, Microsoft Fabric supports MLflow for experiment tracking and provides a model registry with versioning and stages. You can train models using libraries like scikit-learn, LightGBM, XGBoost, and PyTorch while tracking runs.

Yes, you can integrate Power BI semantic models with machine learning predictions in Fabric using Semantic Link. You can also apply PREDICT for batch scoring directly in the warehouse to operationalize your models.

The best way to deploy batch scoring for ML models in a Fabric warehouse is using the PREDICT function. This allows you to run predictions on registered models directly within your data warehouse environment for production pipelines.

Track machine learning experiments and lineage in Fabric notebooks using the integrated MLflow tracking capabilities. This provides end-to-end reproducibility and governance for Spark-based ML development with pre-installed libraries.