Ship models with confidence on Azure Databricks. In one day, you’ll stand up a minimal, repeatable pipeline: define features with ownership, track experiments, implement model registry promotion gates and deploy with shadow/canary plus rollback runbooks. Add drift/quality monitors, alerts, and cost guardrails so ML won’t surprise SRE. Wire CI/CD via GitHub Actions, generate an “evidence pack” (metrics, lineage, approvals) and adopt a simple promotion checklist. You’ll leave with a repo template, pipeline YAML and a working path from data to decisions, fitting .NET and Python teams who want production results, not just notebooks.