11–20 of 113 posts
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 sta…
Wow this looks perfect for what I need right now - just a bit of lightweight tracking.
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 sta…
It sparks joy in my heart whenever I see shade cast against pandas.
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 sta…
How many data scientists that use Databricks for modeling do you know?
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.
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 sta…
"the API makes Pandas look good" It sparks joy in my heart whenever I see shade cast against pandas.
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 sta…
"the API makes Pandas look good" It sparks joy in my heart whenever I see shade cast against pandas.
Close second is the plotly library.
- 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.)
- and of course checkpoints
- simple archiving of the model (and relevant data)
and all that without much (coding) overhead and only using a shared filesystem (!) And with an easy notebook integration. MLflow just has way to many unnecessary features and is unreliable and complicated. When it doesn't work it's so frustrating, it's also quite often super slow. But I always end up creating something like MLflow when working on an architecture for a long time.
EDIT: having written this...I fell like trying to write my own simple library after finishing the paper. A few ideas have already accumulated in my notes that would make my life easier.
EDIT2: I actually remember trying to use SQLite to manage my models! But the server I worked on was locked down and going through the process to get somebody to install me SQLite was just not worth it. It's also was not available on the cluster for big experiments, where it would be even more work to get it, so I gave up on the idea of trying SQLite.
Earlier quoted context omitted.
"the API makes Pandas look good" It sparks joy in my heart whenever I see shade cast against pandas.
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.