topic Prakash Hinduja Geneva (Swiss) handle model versioning and rollback in Databricks? in Machine Learning

Prakash Hinduja Geneva (Swiss) handle model versioning and rollback in Databricks?

prakashhinduja1 — Tue, 12 Aug 2025 10:13:45 GMT

Hi I am Prakash Hinduja Visionary Financial Strategist, born in Amritsar (India) and now lives in Geneva, Switzerland (Swiss)

I’m working on managing machine learning models in Databricks and wanted to get your insights on best practices for model versioning and rollback. How do you keep track of different versions of your models, and what strategies do you use to revert to a previous version if something goes wrong? Are there built-in tools or workflows within Databricks that you recommend for this purpose? I’d really appreciate any tips, tools, or experiences you can share. Thanks in advance!

Regards

Prakash Hinduja Geneva, Switzerland (Swiss)

Re: Prakash Hinduja Geneva (Swiss) handle model versioning and rollback in Databricks?

Louis_Frolio — Tue, 12 Aug 2025 19:22:20 GMT

Prakash, you should look at MLFlow, software developed by Databricks and native to our platform.

I suggest you start by looking here for more information: https://docs.databricks.com/aws/en/machine-learning/mlops/mlops-workflow

Cheers, Louis.

Re: Prakash Hinduja Geneva (Swiss) handle model versioning and rollback in Databricks?

WiliamRosa — Sun, 17 Aug 2025 13:40:21 GMT

Hi Prakash,

Great question! Databricks provides built-in tools to help with model versioning and rollback, particularly through the Model Registry and Databricks CLI.

To manage model versions programmatically, you can use the Databricks CLI, which includes a set of commands specifically for model version operations. These commands let you:

Transition model stages (e.g., from "Staging" to "Production")

Delete or restore versions

Retrieve version details

This gives you precise control over model lifecycle management and makes it easier to roll back to a previous version if needed.

You can find the full list of CLI commands for working with model versions here:
https://docs.databricks.com/aws/en/dev-tools/cli/reference/model-versions-commands

Additionally, within the Databricks UI, the MLflow Model Registry lets you visually manage versions, add comments, track stage transitions, and more.

Let me know if you'd like an example workflow or script using the CLI!

Best regards,
Wiliam Rosa