We got tired of trying 10 ML models every time we had a new dataset [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
We've been building Arcliq ( Join Here:- https://arcliq.app ) for a while and finally feel comfortable sharing it.
The annoying part of working with a new tabular dataset isn't usually writing the model code. It's everything around it:
cleaning the data, figuring out preprocessing, choosing what models are worth trying, tuning them, and then comparing everything properly.
So we built Arcliq to automate that process.
You upload a tabular dataset and Arcliq:
• handles the preprocessing
• trains multiple models
• compares their performance
• gives you the best-performing model and the results
The goal is pretty simple: go from dataset → working ML model without having to be an ML expert first.
It's still very early and currently focused on tabular data + classical ML. There are definitely things we still need to figure out, which is why we want to get this in front of actual users rather than keep building in isolation.
We are opening a small private beta and would love to get a few people using it and telling me where it falls short.
Join Here:- https://arcliq.app
[link] [comments]
More from r/MachineLearning
-
I turned my dog Jackies Brain into a working AI [P]
Oct 14
-
Event2Vec: additive event embeddings where a linear score on a history decomposes exactly into per-event terms [P]
Oct 14
-
Binary noise classification using CRNN mean teacher algo [R]
Oct 14
-
Localized Neural learning vs backpropagation [D]
Oct 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.