Microsoft Fabric integrates AI-powered analytics, allowing businesses to apply machine learning models for predictive insights. This guide explains how to use AI in Fabric for data classification, forecasting, and anomaly detection.

1. AI Capabilities in Fabric

✅ ML Models in Fabric Notebooks (Python, R, Spark ML)
✅ AutoML for No-Code Machine Learning
✅ Azure OpenAI & Copilot Integration

2. Step-by-Step AI Implementation

Step 1: Create a Fabric Notebook

• Open Microsoft Fabric → Data Science Experience.
• Select Python or Spark ML for model training.

Step 2: Train an AI Model

• Use Fabric’s built-in ML libraries (scikit-learn, TensorFlow).
• Train a classification model on customer data.

pythonCopyEditfrom sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier().fit(X_train, y_train)

Step 3: Deploy AI Model in Power BI

• Publish AI Insights to Power BI using ML Endpoints.
• Use DAX measures for AI-powered visualizations.

3. Best Practices for AI Optimization

🔹 Use GPU-Accelerated Compute for large datasets.
🔹 Implement Feature Engineering to improve model accuracy.
🔹 Apply Explainable AI (XAI) for model transparency.

Conclusion

By leveraging Microsoft Fabric’s AI capabilities, enterprises can automate analytics, forecast trends, and enhance business intelligence.