Databricks Machine-Learning Assistance

I have an active workspace in Databricks and I’m at the stage where the machine-learning pieces need to come together quickly. Models, feature engineering, experiment tracking, and deployment workflows are all in play, but I’m hitting a few roadblocks around best-practice architecture and code efficiency. The goal is to get a clean, reproducible ML pipeline running end-to-end—ideally within days, not weeks.

If you have done similar ML work on Databricks before, please share a brief note describing past projects that align with this scope. Screenshots, GitHub links, or notebook snippets demonstrating your previous Databricks ML pipelines are especially helpful.

Deliverables I’m expecting:
• A working Databricks notebook (or notebook set) that trains, evaluates, and saves the model while logging all artifacts to MLflow.
• Clear guidance or inline comments explaining each critical step so I can maintain it after hand-off.
• A short hand-over session (video or chat) to make sure the solution runs seamlessly in my workspace.

Speed matters here—I’d like to kick things off as soon as we settle the details and have the first usable notebook ready ASAP.