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End-to-end LLMOps quickstart on Databricks (customer support ticket classifier): MLflow ChatAgent on a Foundation Model API endpoint, an evaluation gate that promotes a Champion in Unity Catalog, and a Databricks Asset Bundle that deploys schema, experiment, and jobs with batch + real-time inference. - Folder follows the YYYY-MM-[name] convention - README documents setup, structure, data (30 synthetic tickets, no PII), and licenses - LICENSE.md is an unmodified copy of the repo Databricks license - CODEOWNERS entry added for the new folder I have read the contribution guidelines. No sensitive info, no PII, no external dataset. Pending: SME code review approval and internal approval. Co-authored-by: Isaac
CEDipEngineering
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QuentinAmbard,
alanreese-dbrx,
alexott,
anupkalburgi,
kwulffert23,
matthewmoorcroft and
srinivasadmala
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Co-authored-by: Isaac
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What this is
End-to-end LLMOps quickstart on Databricks — a customer support ticket classifier
that carries an LLM agent through the full lifecycle: data ingestion → agent build →
evaluation (Champion gate) → deployment → batch + real-time inference.
Accompanies an upcoming Databricks Community blog post (JIRA TLC-1077).
Contents
2026-06-llmops-quickstart/(follows theYYYY-MM-[name]convention)ChatAgenton a Foundation Model API endpoint, evaluation gate promoting aChampion alias in Unity Catalog, and a Databricks Asset Bundle deploying schema,
experiment, and jobs (dev/prod targets)
README.md(setup, structure, data note, licenses, blog-link placeholder)LICENSE.md— unmodified copy of the repo's Databricks licenseCODEOWNERSentry added for the new folderGuidelines checklist
YYYY-MM-[folder_name]Blog post link will be added to the README once published.