Hi @AnilKumarM,

Agree with @-werners- here. There isn’t a single 'one true' repo layout we mandate, but there are a few public references that show the patterns Databricks recommends.

For bundles/databricks.yml + multi‑env, you may want to check the Declarative Automation Bundles (DABs) for the concept and YAML structure as a starting point. The reference provided by @Sumit_7 for MLOps Stacks is also very good. It is an opinionated ML project template built on bundles, including repo layout, bundle config, and CI/CD. You can also look at this to understand how to scaffold a stack project.

Those docs + the databricks/mlops-stacks repo effectively give you a reference implementation for where databricks.yml lives (project root) and how to define targets/resources... how to separate ML code (src/... and notebooks) from resource YAML (resources/...) and how to structure env‑specific config inside the bundle rather than hard‑coding it.

For CI/CD and repo structure more generally, try this link. It gives some patterns for "single repo with code + bundle config" vs. "separate repos", with concrete examples.

For code vs. utils vs. notebooks, this page walks through putting notebooks in Git, extracting shared code into modules, and testing it.

Taken together, these do not specify your exact config.yaml / utils layout, but they do illustrate the structures Databricks uses internally for production ML projects and how to connect that to databricks.yml and CI/CD.

I hope this provides some guidance.

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