Who needs MLflow when you have SQLite?
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Who needs MLflow when you have SQLite?
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Re: Who needs MLflow when you have SQLite?
#2Being able to use SQL for later analysis is definitely a good idea. For smaller models SQLite for sure is enough but as soon as you want to scale your HPO across multiple servers or even just processes, you will need something that supports a multi-user database. E.g. Optuna supports PostgreSQL and also defaults to SQLite as far as I know.
Re: Who needs MLflow when you have SQLite?
#3Yeah, MLFlow is a shitshow. The docs seem designed to confuse, the API makes Pandas look good and the internal data model is badly designed and exposed, as the article says.
But, hordes of architects and managers who almost have a clue have been conditioned to want l and expect mlflow. And it's baked into databricks too, so for most purposes you'll be stuck with it.
Props to the author for daring to challenge the status quo.
Re: Who needs MLflow when you have SQLite?
#4The elephant in the room with data is that we don’t need a lot of the fancy and powerful technology. SQL against a relational database gets us extraordinarily far. Add some Python scripts where we need some imperative logic and glue code, and a sprinkle of CI/CD if we really want to professionalise the work of data scientists. I think this covers the vast majority of situations.
Despite being around it for some time, I’m not sure big data or machine learning needed to be a thing for the vast majority of businesses.
Re: Who needs MLflow when you have SQLite?
#5No post body was provided.
Re: Who needs MLflow when you have SQLite?
#6Could you provide context on why SQLite would replace MLflow? From the standpoint of model tracking (record and query experiments), projects (package code for reproducibility on any platform), deploy models in multiple environments, registry for storing and managing models, and now recipes (to simplify model creation and deployment), MLflow helps with the MLOps life cycle.
Re: Who needs MLflow when you have SQLite?
#7Wow this looks perfect for what I need right now - just a bit of lightweight tracking.
Re: Who needs MLflow when you have SQLite?
#8The elephant in the room with data is that we don’t need a lot of the fancy and powerful technology. SQL against a relational database gets us extraordinarily far. Add some Python scripts where we need some imperative logic and glue code, and a sprinkle of CI/CD if we really want to professionalise the work of data scientists. I think this covers the vast majority of situations. Despite being around it for some time,…
Yeah and even if you do need to do proper big-dataset-ML... a SQL box and maybe something like a blob storage for large artifacts (S3, Azure storage account, whatever) is all you need as well. But if your boss bought The MLOps Experience, you gotta do what the cool kids are doing!
Re: Who needs MLflow when you have SQLite?
#9Could you provide context on why SQLite would replace MLflow? From the standpoint of model tracking (record and query experiments), projects (package code for reproducibility on any platform), deploy models in multiple environments, registry for storing and managing models, and now recipes (to simplify model creation and deployment), MLflow helps with the MLOps life cycle.
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
Re: Who needs MLflow when you have SQLite?
#10Could you provide context on why SQLite would replace MLflow? From the standpoint of model tracking (record and query experiments), projects (package code for reproducibility on any platform), deploy models in multiple environments, registry for storing and managing models, and now recipes (to simplify model creation and deployment), MLflow helps with the MLOps life cycle.
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