In this live, code‑forward session on Azure Databricks, I will ingest messy enterprise text (tickets, emails, notes), normalize and label it, and build task‑ready representations: TF‑IDF/n‑grams, domain keywords and lightweight transformer embeddings. I will compare classical models vs small transformers, show evaluation that survives drift and wire outputs into downstream apps and dashboards. You’ll see working notebooks, Delta tables, and a simple “NLP scorecard” for precision/recall, latency and cost. Leave with reusable patterns and a starter repo to ship explainable, cost‑aware NLP on Databricks.