Challenges in Deploying Machine Learning Models into Production Workflows

Hi everyone,

I am exploring how teams are moving machine learning models from experimentation into reliable production workflows.

In many projects, the challenge is not only training the model but also managing the complete lifecycle:

- Preparing and maintaining quality datasets

- Tracking experiments and model versions

- Deploying models for real-world usage

- Monitoring performance after deployment

- Handling model updates over time

I would like to understand how the community is approaching this with Microsoft Fabric.

A few questions:

What are the recommended patterns for managing ML model lifecycle in Fabric?

How are teams handling model versioning and experiment tracking?

Are you using Fabric notebooks, MLflow, or external platforms for managing production ML workflows?

What challenges have you faced when moving data science projects from development to production?

Would love to hear practical experiences and approaches from the community.