5 ms·
Yeah, 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
by isoprophlex 4y ago
Yeah, 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.
- idomi 4y agoHow many data scientists that use Databricks for modeling do you know?
- dachryn 4y agoits forced upon many of them that are in finance, banking, insurance, ... Mainly because those tend to run on Microsoft Azure, which has no decent analytics offering, and are pushing Databricks extremely hard. The CTO or whatever just pushes databricks. On paper it checks all the boxes. Mlops, notebooks, experiment management. It just does all of those things very badly, but the exec doesn't care. They only care about the microsoft credits. Just to avoid using Jupyter so the compliance teams stay happy as well because Microsoft sales people scared them away from from open source.
- nerdponx 4y agoMy team very nearly had this happen to us. We pushed back on it very, very, very hard, and finally convinced "IT" to not turn off our big Linux server running JupyterHub. We actually ended up using Databricks (PySpark, Delta Lake, hosted MLFlow) quite a bit for various purposes, and were happy to have it available. But the thought of forcing us into it as our only computing platform was a spine-chilling nightmare. Something that only a person who has no idea what data analysts and data scientists actually do all day would decide to do.
- akdor1154 4y agoWhat would you go with instead for collaborative notebooks? I ask because normally I tend pretty strongly towards the "NO just let the DSes/analysts work how they want to", which in this case would be running Jupyter locally. However DBr's notebooks seem genuinely useful. Is your issue "but I don't need Spark" or "i wanna code in a python project, not a notebook?", or something else? Imo if DBr cut their wedding to Spark and provided a Python-only nb environment they'd have a killer offering on their hands.
- FridgeSeal 4y ago> What would you go with instead for collaborative notebooks? Production workloads should be code. In source control. Like everybody else. Notebooks inevitably degrade into confusing, messy blocks of “maybe applicable, maybe not” text, old results and plots embedded in the file because nobody stripped them before committing and comments like “don’t run cells below here”. They’re acceptable only as a prototyping and exploration tool. Unfortunately, whole “generation” of data scientists and engineers have been trained to basically only use notebooks.
- isoprophlex 4y agoIt'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
- idomi 4y agoPretty 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.
- chaps 4y ago"the API makes Pandas look good" It sparks joy in my heart whenever I see shade cast against pandas.
- avaldez_ 4y agoWhat's wrong with pandas? Honest question. I'm a bit new to ML. Also, what's the alternative?
- lordgroff 4y agoEvery 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.
- laichzeit0 4y agoTo be fair, taking R to production is a goddamn nightmare.
- chaxor 4y agoR 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.
- lordgroff 4y agoYep, 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.
- mostdataisnice 4y agoWhere does the article say that?
- isoprophlex 4y agoAbout exposing the data inside MLFlow > I found the query feature extremely limiting (if my experiments are stored in a SQL table, why not allow me to query them with SQL).