mlops-quickstart

Adapt the MLOps Quickstart Databricks Asset Bundle (databricks.yml, resources/*.yml) and CI/CD pipelines (Azure DevOps, GitHub Actions). Use when the user wants to rename the bundle, change catalog/schema per environment, add a staging target, configure permissions, switch from serverless to job clusters, or wire up secrets and branch triggers for deployment.

Adapt the MLOps Quickstart data ingestion notebook from the Iris placeholder to a custom data source (cloud storage, JDBC, API, Delta table, etc.). Use when the user wants to ingest their own dataset, swap the source data, rename the feature table, add bronze/silver/gold preprocessing stages, or extend `1_data_preprocessing/`.

Adapt the MLOps Quickstart inference notebooks (batch and realtime / serving endpoint) to a custom model and input schema. Use when the user wants to run batch scoring on new data, query the realtime serving endpoint, change the inference output table, or enable Lakehouse Monitoring on inference results.

Adapt the MLOps Quickstart MLflow 3 deployment pipeline (evaluate → approve → deploy) to a custom model. Use when the user wants to change evaluation metrics or thresholds, customize the human-in-the-loop approval gate, adjust the serving endpoint configuration, or wire a freshly registered model version to the deployment job.

Adapt the MLOps Quickstart model training notebook from the Iris classifier placeholder to a custom algorithm and problem type (regression, clustering, classification, forecasting). Use when the user wants to change the model, features, target, metrics, or registered model name, or when they ask how to fit a different ML problem into this template.

Manage Python dependencies for the MLOps Quickstart via the root `requirements.txt` file. Use when the user wants to add, remove, or upgrade a Python package, pin or unpin a library version, fix a dependency conflict, or understand how notebooks install their dependencies.

Reference for the Databricks MLOps Quickstart repo structure, parameterization contract, and Challenger/Champion conventions. Use whenever the user asks about repo layout, naming patterns, three-level Unity Catalog references, notebook widgets, MLflow experiment naming, or serving endpoint naming, or before adapting any notebook, job, or bundle in this repo.