Show HN: MLConsole – web app to train ML models, for free and client-side

1) The target user is: a freelance marketer, small/medium enterprise without dedicated data scientists, technical people (e.g. engineers) from other fields, or without extensive stats/ml knowledge.

2) RE:lack of hand-holding. This is likely the biggest challenge for this project: showing people how to think about ML, and how to use it to derive value for their business, without going through hours of training or lengthy tutorials.

> - Guidance on what consitutes good data and how to structure it for input

> - Examples of how it might be applied to their use case Most users are stuck at this initial phase (preparing the data).

One thing I'm working now is adding use-case focused guides: short article explaining how a realtor would go about building a model to help them roughly value houses (including data collection). I hope this helps with these two points.

> Help interpreting of whether the MAE / loss is good or bad

There's a couple things I'm working on that might help: 1) Show metric improvement relative to a baseline (e.g. MAE for a model that always predicts the mean). 2) Show both train and test curves. The current curve is only on test data.

> Better understanding of when to use regression vs. classification

> Some sense of how training and validation is done

I'm currently redesigning the UX around a step-by-step flow (for initial users at least), that should give a bit of room to explain things along the way (e.g. what classification/regression means for total beginners).

> Some automatic way to prevent overfitting

Medium-term models there'll be a mode to continuously train models to tune hyperparameters, that should help avoid overfitting. Until then it's mostly handpicked parameters (including regularization), and having tested this on Kaggle challenges it still sometimes beats my hand-written ML code :)

> Model weights

You can already download the model weights (download icon next to the model name) ; or do you mean feature importances? That's a planned feature, but it's not straightforward to implement in a generic way so might take a month or two before it's shipped.

Knowing the underlying math and engineering is of course still useful for those that want to go the extra step (squeezing out .5% accuracy, or training a model on 1TB of data), but for all normal people out there ML should be as simple as loading up a webpage and dropping your data.

Thanks for the support!

RE:bias/explainability, that's indeed the main argument against asking people to use ML as a black-box.

I think fully understanding the very nuanced biases that can sneak in data (e.g. selectivity bias from some events being more represented in records, let's say) does require a keen sense for data, which an automated tool likely cannot provide. My bet is that this is not enough of a reason to block people from at least dipping their toe in the ML world, and that we can do more education down the line (e.g. about evaluating systems in real-life, to at least catch underperformance before looking for its source) to solve these cases.

On explainability, some basic tools (e.g. feature importance) are in the roadmap, I hope to get to it in this quarter!

RE:creating datasets being the hardest part of the job, not modeling... well, I think you're 100% right. And that's a tougher nut to crack.

One thing I'm planning to do to help here is to provide a number of "templates", e.g. concrete use cases that people can piggy back on. e.g. explain to realtors that they can estimate house prices by creating a spreadsheet with features A,B,C and D. I can't do this for every imaginable use case, but I hope this is enough to at least inspire people on how to think about data and ML.

I see this as addressing a different stage (rapid/cheap prototyping) and audience (less technical people, smaller enterprises); it'll never be as powerful & state-of-the-art as advanced ML tools by cloud providers, and that's OK :)

> I'll give this a try, though I'm not sure what strategy it's using (I'm assuming an ensemble?)

It's embarrassingly simple right now: a fully-connected DNN with a static architecture (hand-picked because it worked fine for all datasets I tried), but you can also enable ensembling and/or change the network's architecture (just click on "Show advanced settings")

> Also you're asking the user if the objective should be classification or regression, I think that should be automatic, it's already buzzwords for your typical marketing person :)

Good point, but it's not always something that can be determined automatically. I did add some heuristics to warn people when we think they are wrong (e.g. running classification on a numeric variable with too many unique values, usually the sign it should be a regression problem instead).

One thing that might help here is that the redesigned flow shows a step-by-step instructions where one step is to pick the task (regression vs classification), and we'll add some text there to help people understand what the difference is. I'll also consider the heuristic approach going forward! (and make sure it doesn't override cases where users already manually set the task)

You can train a model in <30s with ML Console. Two clicks: 1 to pick a dataset, 2nd to click on "train a model". No wait time or sign-up required.

There's a very good reason for this: ML Console uses a very different approach, as all computations (data pre-processing, model training, etc.) is written in Javascript and runs in the client.

This makes the app orders of magnitude more responsive than any competitor (and hosting it is so cheap that I'm offering it for free during this beta phase, something these services wouldn't be able to afford).

With that being said, cloud-based solutions like these will always have their place when you need to train on terabytes of data, or squeeze out 1% higher accuracy with some state-of-the-art models.