Difference between MLFlow recipes and projects?

[smedegaard]

MLFlow projects are described as

An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects, making it possible to chain together projects into workflows.

MLFlow Rcipes (previously "pipelines") are described as

MLFlow Recipes is a framework that enables data scientists to quickly develop high-quality models and deploy them to production. Compared to ad-hoc ML workflows, MLflow Recipes offers several major benefits...

They see to serve the same purpose.

• What's the difference?
• When does it make sense to use one over the other?

[Priyag1 · Accepted solution]

@Anders Smedegaard Pedersen​ Each project is simply a directory of files, or a Git repository, containing your code whereas  recipe is an ordered composition of Steps used to solve an ML problem or perform an MLOps task, such as developing a regression model or performing batch model scoring on production data. MLflow Recipes provides APIs and a CLI for running recipes and inspecting their results.

Here a Step represents an individual ML operation, such as ingesting data, fitting an estimator, evaluating a model against test data, or deploying a model for real-time scoring. Each Step accepts a collection of well-defined inputs and produce well-defined outputs according to user-defined configurations and code.

Hope this helps you.

[Anonymous]

Hi @Anders Smedegaard Pedersen​ 

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[smedegaard]

Thanks for the answer @Priyadarshini G​ . Although a project has a pre-defined folder structure and standard files, it also "... includes an API and command-line tools for running projects, making it possible to chain together projects into workflows." and we can "run multi-step workflows" with projects, either with the cli or mlflow.projects.run()

"Recipe Templates are git repositories with a standardized, modular layout.". In a recipe we define the flow in recipe.yaml, define our steps in python files in the ./steps folder, and profiles and tests in the corresponding folders

they might have slightly different interfaces, but they still seem to cover 95% of the same needs?

Please enlighten me if there is something I am not seeing.