Building ML Models in Microsoft Fabric
Train and deploy machine learning models using Fabric Data Science capabilities.
Microsoft Fabric Data Science provides a complete environment for building, training, and deploying machine learning models at enterprise scale.
What is Fabric Data Science?
Fabric Data Science brings together notebooks, MLflow integration, and model deployment capabilities in a unified experience. It supports popular frameworks and integrates directly with OneLake data.
Getting Started with ML in Fabric
Step 1: Create a Notebook In your Fabric workspace, create a new notebook. Notebooks support Python, PySpark, and R for data science workloads.
Step 2: Load Data from OneLake Access your Lakehouse data directly: - Use Spark DataFrames for large datasets - Leverage Delta Lake for versioned data - Connect to semantic models for prepared features
Step 3: Train Your Model Fabric supports all major ML frameworks: - Scikit-learn for traditional ML - PyTorch for deep learning - TensorFlow for neural networks - XGBoost for gradient boosting
MLflow Integration
Fabric natively integrates MLflow for experiment tracking: - Log parameters, metrics, and artifacts automatically - Compare model runs in the Experiments UI - Register best models in the Model Registry - Deploy models with one click
Model Deployment
Once trained, deploy models as: - Real-time endpoints for predictions - Batch scoring jobs on large datasets - Integration with Power BI for embedded predictions
Best Practices
- Start with exploratory analysis to understand your data
- Use MLflow autologging to capture all experiments
- Version your training data with Delta Lake
- Implement feature engineering in reusable notebooks
- Monitor model performance in production
Frequently Asked Questions
What ML frameworks does Fabric support?
Fabric supports Scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, and other Python-based ML libraries. You can install additional packages as needed.
Can I use AutoML in Microsoft Fabric?
Yes, Fabric includes automated machine learning capabilities that can automatically select algorithms, tune hyperparameters, and generate feature engineering suggestions.