ML Deployment

[binitafulpagare]

Hi everyone,

I'm learning how organizations move machine learning models from experimentation to production using Microsoft Fabric.

For those working in production environments:

How do you deploy models?
How do you monitor model performance?
How do you manage retraining?
Which Fabric services do you use throughout the ML lifecycle?

I'd appreciate hearing about your architecture and best practices.

Thank you!

[nbleonhard]

Before Microsoft Fabric, our entire machine learning operation ran on my laptop. One of the big advantages of moving to Fabric was that each ML project got access to its own lakehouse, which now plays a central role in our entire ML lifecycle.

Here are a few of the biggest benefits we've seen:

• Decoupled workflows: It allowed us to decouple our machine learning pipeline from the data ingestion process.
• Reference tables: Setting up reference tables is super convenient. For example, I use a delta table as a crosswalk to map our raw feature names to user-friendly names.
• BI accessibility: Storing our current predictions (as well as past predictions) directly on the lakehouse is convenient for our BI team.

[v-abhinavmu]

Hi @binitafulpagare,

Thanks for reaching out to the Microsoft Fabric Community forum.

We've started following the MLflow-native workflow that Microsoft recommends for Fabric.

Our typical lifecycle looks like this:

OneLake/Lakehouse → Fabric Notebook (Spark) → MLflow Experiment → ML Model Registry → ML Model Endpoint → Monitoring Hub → Fabric Pipeline (scheduled retraining)

• Development: Train models in Fabric notebooks and track every run with MLflow Experiments, which logs parameters, metrics, artifacts, and code versions.
• Deployment: Register the best model in the ML Model Registry and activate a Machine Learning Model Endpoint for real-time inference.
• Monitoring: We use the Monitoring Hub to track experiment runs and monitor endpoint metrics such as request count, error count, latency, and traffic. For business KPIs (accuracy, drift, etc.), we'd typically implement our own monitoring notebooks or pipelines since Fabric's built-in monitoring focuses on experiment and endpoint operations rather than automated drift detection.
• Retraining: Scheduled Fabric Pipelines rerun training notebooks, log the new run to MLflow, evaluate the results, and register a new model version when it meets our acceptance criteria.
• Production MLOps: For enterprise environments, cross-workspace MLflow logging supports Dev → Test → Prod promotion while maintaining separate workspaces and governance.

Microsoft has done a nice job of integrating experimentation, model management, deployment, and monitoring into a single platform, making it possible to implement an end-to-end MLOps workflow without relying on multiple external services.

For more details, please refer to the below offical documentation:

Machine learning experiment - Microsoft Fabric | Microsoft Learn

Serve real-time predictions with ML model endpoints (Preview) - Microsoft Fabric | Microsoft Learn

Monitor machine learning experiments and models - Microsoft Fabric | Microsoft Learn

I hope this helps. Please feel free to reach out if you have any further questions.
Thank you.