Great updates! I've been using Genie Code heavily for ML engineering in Databricks and the productivity gain is real.

Just finished building a full RFM segmentation model for supplier performance analysis end-to-end inside a notebook — feature engineering with Feature Store, K-Means training with Optuna hyperparameter optimization, MLflow experiment tracking, model registration in Unity Catalog, and segment profiling — all with Genie Code helping at each step.

What impressed me most is how context-aware it is: it reads your existing tables, understands the schema, suggests code that actually fits your data, and catches issues before they become bugs. It also handles the Unity Catalog + MLflow integration patterns correctly out of the box, which is usually the trickiest part.

For anyone studying for the ML Engineer Associate cert or building production ML pipelines on Databricks, I'd strongly recommend trying it. It doesn't just write boilerplate — it understands the full ML lifecycle.