Say Goodbye to Messy Data Science Notebooks with MLFlow Recipes (Pipelines)!

MLFlow Recipes is the ultimate solution for managing your end-to-end machine learning workflow through a “template”, that comes with a ready-to-go file and folder structure for any regression or binary classification problem. This folder structure includes everything, from library requirements, configuration, notebooks, and tests, that’s needed to make a data science project reproducible and production-ready.
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reference github:
github.com/mlflow/recipes-exa...
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[@gopinathrajee]

Very interesting session on MLFlow Recipes. The git has been better organized since this video though for those watching this video late.

[@torito_misiu]

Thank you

[@Balajinicky]

Can you make a video explaning mlflow recipes use in the Deep learning project and its preformance?

[@jevigudaganavar4258]

Can you please help with the classification template?

[@firasjolha1282]

I think it's a redundancy, especially if you have some established processes to DataOps

[@sivasankarikrishnamoorthy6245]

kindly make dbx databricks lab

[@MG_cafe]

Great idea, thanks!

[@shanky1897]

Can you please create video which combines ml-flow recipes with your databricks mlops + github actions video (ua-cam.com/video/f2XQMFod8kg/v-deo.html). That will showcase how can we standardized complete development and deployment process together.