Skip to content

chore: improve README #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
pedrozanlorensi merged 1 commit into from
Feb 24, 2026
Merged

chore: improve README #3

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 3 additions & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,9 @@ Having a *Continuous Deployment (CD)* pipeline enabled means that if you make an
- Check out the [Big Book of MLOps](https://www.databricks.com/resources/ebook/the-big-book-of-mlops).
- Here you can find more info on how to manage different environments for MLOps: [MLOps Workflows](https://docs.databricks.com/aws/en/machine-learning/mlops/mlops-workflow).
- If you don't want to retrain your models in each environment, you can **promote** your models across environments. Check out this documentation for more info: [Promote a model across environments](https://docs.databricks.com/aws/en/machine-learning/manage-model-lifecycle#promote-a-model-across-environments).
- This is a more advanced template for MLOps, called the "MLOps Stacks" template: [MLOps Stacks: model development process as code](https://docs.databricks.com/aws/en/machine-learning/mlops/mlops-stacks). It may be worth checking it as you mature and grow in your MLOps journey!
- If you are interested in Unit, Integration, or User Acceptance Testing, I recommend watching the "Testing Strategies with Databricks" module of the [Advanced Machine Learning Operations](https://customer-academy.databricks.com/learn/courses/3508/advanced-machine-learning-operations) training, from the Databricks Academy.
- Want to vibe-code your Databricks solutions? Check out the [AI Dev Kit](https://github.com/databricks-solutions/ai-dev-kit)! You can use the skills implemented by it along with this repo to advance your solutions using AI-Driven Development.
- Check out the "MLOps Stacks" template for a more advanced implementation: [MLOps Stacks: model development process as code](https://docs.databricks.com/aws/en/machine-learning/mlops/mlops-stacks). It may be worth checking it as you mature and grow in your MLOps journey!

## Q&A
- **Can I specify who I want the approvers to be in my deployment job?** Yes, you restrict who has permissions to **APPLY TAGs** and also set a [governed tag policy](https://docs.databricks.com/aws/en/admin/governed-tags/) so only certain people are allowed to set a specific tag value (in this case, it will be the "approval" tag).
Expand Down