databricks-solutions/mlops-quickstart

mlops-quickstart-overview

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…

adapt-bundle-and-cicd

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…

adapt-data-ingestion

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…

adapt-inference

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…

adapt-model-deployment

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…

adapt-model-training

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…

manage-dependencies

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…