Best practices for building machine learning pipelines in Microsoft Fabric?

Great questions! A workflow that has worked well for many Fabric users is:

• Organize your project by layers rather than by notebooks. A common structure is:

  • Bronze: Raw data ingestion

  • Silver: Cleaned and transformed data

  • Gold: Feature-ready datasets for ML

  • Separate notebooks for data exploration, feature engineering, training, evaluation, and inference.

• Choose the right tool for data preparation.

  • Use Dataflows Gen2 for reusable low-code ETL and business transformations.

  • Use Spark notebooks for feature engineering, large-scale data processing, and ML-specific preprocessing.

  • Store curated datasets in Lakehouse or Warehouse depending on downstream requirements.

• Track experiments and models.

  • Use MLflow, which is integrated with Microsoft Fabric, to log parameters, metrics, artifacts, and register models. This makes it much easier to compare experiments and reproduce results.

• Deployment and monitoring.

  • Validate models on a holdout dataset before deployment.

  • Version both your data and models.

  • Automate retraining with Fabric Pipelines when appropriate.

  • Monitor prediction quality, latency, and potential data drift, and retrain when performance degrades.

• General best practices.

  • Keep notebooks modular and focused on a single task.

  • Use Git integration for source control.

  • Parameterize notebooks instead of hardcoding values.

  • Build reusable feature engineering logic where possible.

  • Document assumptions, data lineage, and model versions.

I'm also interested in learning from production deployments.How are others handling CI/CD, model monitoring, and MLOps in Microsoft Fabric? Any real-world architecture diagrams, lessons learned, or recommended patterns would be greatly appreciated!