SQLite is literally a backend for MLflow, so the argument being made really is that you should just use SQL when you can, which is kind of adjacent to any criticisms of MLflow
Who needs MLflow when you have SQLite?
21–30 of 113 posts
Re: Who needs MLflow when you have SQLite?
#22Yeah, 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 sta…
Where does the article say that?
Re: Who needs MLflow when you have SQLite?
#23Yeah, 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 sta…
"the API makes Pandas look good" It sparks joy in my heart whenever I see shade cast against pandas.
What bothers me the most is the egregious data types for any argument.
If it's a string, do this. If it's a list, do that. If it's a dictionary of lists, do this other thing.
No, I want you to force me to provide my data in the right way and raise a noisy exception if I don't.
Re: Who needs MLflow when you have SQLite?
#24Yeah, 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 sta…
How many data scientists that use Databricks for modeling do you know?
It's ubiquitous. I've consulted for a 100 person company that built a data product on top of some IoT data. Everything was in databricks, literally everything. (Not endorsing that, just an observation)
Talking to a 2000+ person org now that is standardizing data science across the org using... you guessed it
Re: Who needs MLflow when you have SQLite?
#25I don't get why a lot of people are calling mlflow a shitshow when it has done so much getting data scientist out of recording experiments via CSV. I can log models and parameters and use the UI to track different runs. After comparisons, I can use the registry to register different staging. If you have other model diagnostic charts you can log the artifact as well. I think mlflow v2 has auto logging included so why all the fuss?
Re: Who needs MLflow when you have SQLite?
#26I think MLflow is a good idea (very) badly executed. I would like to have a library that combines: - simple logging of (simple) metrics during and after training - simple logging of all arguments the model was created with - simple logging of a textual representation of the model - simple logging of general architecture details (number of parameters, regularisation hyperparameters, learning rate, number of epochs etc…
Sqlite is in python’s stdlib, so how can this be an issue? Was there no local filesystem whatsoever?
Re: Who needs MLflow when you have SQLite?
#27Earlier quoted context omitted.
Every time I open up pandas I jealously remember the expressive beauty of R for these tasks. But because we're all "serious" of course we must use Python for production lest we not be serious.
To be fair, taking R to production is a goddamn nightmare.
R is a trash of a language. It doesn't have any sense of coherency to it at all.
They keep trying to fix the underlying problems by ducktaping paradigms on to it over and over (S3, S4, R6, etc). There's never a clear sense of the best way to do anything, but plenty of options to do a thing in a very hacky 'script-kiddy' way.
Looking out at the community of different projects it becomes clear that everyone is pretty lost as to what design principles should be used for certain tasks, so every repo has its own way of doing things (I know personal style occurs in other languages, but commonalities are much less recognizable in R projects).
It's tragic that such a large community uses it.
Re: Who needs MLflow when you have SQLite?
#28Earlier quoted context omitted.
To be fair, taking R to production is a goddamn nightmare.
R is a trash of a language. It doesn't have any sense of coherency to it at all. They keep trying to fix the underlying problems by ducktaping paradigms on to it over and over (S3, S4, R6, etc). There's never a clear sense of the best way to do anything, but plenty of options to do a thing in a very hacky 'script-kiddy' way. Looking out at the community of different projects it becomes clear that everyone is pretty l…
Yep, this is a mark of someone that's never used R but has heard a lot of incredibly ill informed criticism around it.
One look of dplyr code over pandas would of course disabuse anyone of the notion that R is trash and the tragedy is Python will in the current state never have anything like that. That's the advantage of the language being influenced by Lisp vs not.
Re: Who needs MLflow when you have SQLite?
#29Earlier quoted context omitted.
How many data scientists that use Databricks for modeling do you know?
It's ubiquitous. I've consulted for a 100 person company that built a data product on top of some IoT data. Everything was in databricks, literally everything. (Not endorsing that, just an observation) Talking to a 2000+ person org now that is standardizing data science across the org using... you guessed it
Pretty interesting. I think this is part of this notion to release half baked products, like some of the stuff in there are really cool, just enough to get you in but it doesn't scale and usually is complex to deploy/use.
Re: Who needs MLflow when you have SQLite?
#30I think MLflow is a good idea (very) badly executed. I would like to have a library that combines: - simple logging of (simple) metrics during and after training - simple logging of all arguments the model was created with - simple logging of a textual representation of the model - simple logging of general architecture details (number of parameters, regularisation hyperparameters, learning rate, number of epochs etc…
I'm happy to collaborate with you, let's build the best experiment tracker out there! Feel free to ping me at eduardo@ploomber.io