ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML

About this episode

ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML

Episode 22 • 2026-05-29

Microsoft Fabric ships its own MLflow registry — but is it a replacement for Azure Machine Learning? Matthias and Fabia work through the four-layer registry model, PREDICT versus Model Endpoints, the Direct Lake prediction loop, and the architectural question that actually determines the answer: where do your predictions land?

What we discuss

• A real-world mistake from a pre-Fabric era
• The one question that reframes the architectural debate
• How we got here — predecessor products and evolution
• Why the "obvious" answer is often wrong
• A real Reddit/Microsoft Q&A question unpacked
• The concrete recommended architecture
• F-SKU realism — what this actually costs
• When the rejected approach is actually right
• Risks of the recommended path
• What Microsoft is shipping that changes the calculus
• The architectural principle to take home

Key takeaways

• Where do the predictions land. That question answers the architecture. OneLake plus Power BI Direct Lake — Fabric ML Model, genuinely the right call. REST API for an app — evaluate Endpoints maturity or route to Azure ML. GPU training,...
• I'd go further. Already on Databricks with Unity Catalog? Don't migrate. Fabric ML Model is not a migration target for Databricks shops — the platform maturity gap is real. The hybrid that actually works: train on Azure ML with GPU,...
• For Power BI shops — yes. PREDICT writes predictions to a Delta table in OneLake, Direct Lake reads it with zero copy, zero scheduled refresh. That eliminates an entire class of ETL work. But only if Power BI is your audience.

Resources

• ML Experiment
• Notebooks
• Lakehouse
• Direct Lake
• Code-first AutoML
• Low-code AutoML
• SynapseML
• Activator
• Machine learning model in Microsoft Fabric
• What is Data Science in Microsoft Fabric?
• Tutorial Part 3: Train and register a machine learning model
• Tutorial Part 4: Perform batch scoring and save predictions
• Machine learning model scoring with PREDICT
• Serve real-time predictions with ML model endpoints (Preview)
• Train models with scikit-learn in Microsoft Fabric

About the show

Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.

Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.

Submit your case

Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.

Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.