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Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

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Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#31

Post by danott »

gamerDude wrote: post #29

This is a very interesting product, and since I have some data that could really benefitted from this, I tried it out. I went through the upload process. But then I don't really know what to do from there. I tried some filters. I went to the invoke page, but I had no idea what invoke does or what the example output is. (Eventually figured out that I can just put text in the invoke and run it). All in all, there are a…

Thanks for the feedback! We actually had a feature for “what is the accuracy if I only consider >80% confident samples” but we iterated away from it because people found it complicated. We’ll definitely bring it back when we can make it simple enough.

We’ve also found that people can get lost in the filters; in particular the “Not assigned” annotation filter we probably need to remove for people who have annotated all of their data.

In terms of separating training / test data: we use cross-validation so that we can abstract away the concept of train vs. test vs. validate sets.

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#32

Post by gamerDude »

saintarian wrote: post #30
gamerDude wrote: post #29

This is a very interesting product, and since I have some data that could really benefitted from this, I tried it out. I went through the upload process. But then I don't really know what to do from there. I tried some filters. I went to the invoke page, but I had no idea what invoke does or what the example output is. (Eventually figured out that I can just put text in the invoke and run it). All in all, there are a…

Thanks for trying us out and for the feedback! I agree that our filters are a little confusing right now and we're working on fixing it. In the meantime, here are a couple of filters you could try: - To see all cases where the model disagrees with your annotation: Function Output = Disagrees, Desired Output = Any. - To see the least confident predictions from the model: Function Output = Any, Desired Output = Any, So…

So, yeah. I could actually use some help on language here. Is Desired Output what I tagged it as?

I think output is confusing me a bit. Output being predicted value? And then desired output is user tagged value?

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#33

Post by saintarian »

gamerDude wrote: post #32
saintarian wrote: post #30

Earlier quoted context omitted.

Thanks for trying us out and for the feedback! I agree that our filters are a little confusing right now and we're working on fixing it. In the meantime, here are a couple of filters you could try: - To see all cases where the model disagrees with your annotation: Function Output = Disagrees, Desired Output = Any. - To see the least confident predictions from the model: Function Output = Any, Desired Output = Any, So…

So, yeah. I could actually use some help on language here. Is Desired Output what I tagged it as? I think output is confusing me a bit. Output being predicted value? And then desired output is user tagged value?

Desired Output is what you tagged it as. Function Output is what the model predicted.

We tried to make the lingo developer-friendly. We think of models as functions that transform inputs to outputs. Instead of writing code to do so, as developers usually do, you train the function by providing desired outputs to sample inputs.

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#34

Post by pyrrhotech »

This looks cool, I signed up. I may have to employ this in my algotrading pipeline. I've collected historical data from 100s of different indicators and I'm currently using a largely heuristic approach with some light ML on subsets, but so far I haven't had the ML breakthrough I've been looking for, https://grizzlybulls.com/models/vix-ta-macro-advanced

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#35

Post by saintarian »

pyrrhotech wrote: post #34

This looks cool, I signed up. I may have to employ this in my algotrading pipeline. I've collected historical data from 100s of different indicators and I'm currently using a largely heuristic approach with some light ML on subsets, but so far I haven't had the ML breakthrough I've been looking for, https://grizzlybulls.com/models/vix-ta-macro-advanced

Thanks for trying us out. We just added a beta for classifying tabular inputs (a mixture of text and numbers) - this may be of interest to you. We have seen some people use our platform to detect stock market trends. Let us know how it goes and reach out if we can help (george at nyckel dot com).

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#36

Post by psimm »

Very cool! I signed up and uploaded data for a text classifier. 3000 examples of social media posts on a binary annotation task. Got 91% initially, then looked through the annotations and corrected a few errors that had snuck in. The UI for that is great. That got it to 92%.

Easy to use UI, easy data upload and the training was quick. A great tool for testing new ideas for classifiers. For bigger projects I'd be concerned about long term cost with pay per invocation.

Is weak labeling via labeling functions (snorkel, skweak) something that's on the roadmap for Nyckel? Also, do you plan to add named entity recognition?

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#37

Post by saintarian »

psimm wrote: post #36

Very cool! I signed up and uploaded data for a text classifier. 3000 examples of social media posts on a binary annotation task. Got 91% initially, then looked through the annotations and corrected a few errors that had snuck in. The UI for that is great. That got it to 92%. Easy to use UI, easy data upload and the training was quick. A great tool for testing new ideas for classifiers. For bigger projects I'd be conc…

Thanks you for the kind words and feedback! You basically went through most of the UI flow that we designed for. You're spot-on about testing new classifiers - answering the question "Can ML even help with my problem?" is much easier with Nyckel and prototyping and rapid iteration starts with that.

Our goal is to be cost-competitive, even for bigger projects. Given how early we are, our pricing structure is still being worked on, especially for high-volume.

Integrating with labeling solutions is in our roadmap. In the meantime, our API should enable any data/labeling integrations.

Named entity recognition is also in the roadmap. Would love to hear more about your use-case and we can give you access to the beta when ready.

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#38

Post by beijbom »

saintarian wrote: post #37
psimm wrote: post #36

Very cool! I signed up and uploaded data for a text classifier. 3000 examples of social media posts on a binary annotation task. Got 91% initially, then looked through the annotations and corrected a few errors that had snuck in. The UI for that is great. That got it to 92%. Easy to use UI, easy data upload and the training was quick. A great tool for testing new ideas for classifiers. For bigger projects I'd be conc…

Thanks you for the kind words and feedback! You basically went through most of the UI flow that we designed for. You're spot-on about testing new classifiers - answering the question "Can ML even help with my problem?" is much easier with Nyckel and prototyping and rapid iteration starts with that. Our goal is to be cost-competitive, even for bigger projects. Given how early we are, our pricing structure is still bei…

Chiming in on the weak labeling question: As of right now, you can use outside libraries like skweak to create weak labels offline and then PUT those using our API (https://www.nyckel.com/docs#update-annotation). This wouldn't cost anything since we only charge for invokes, but it requires some work.

We may look at adding weak labeling as a first class feature of our site down the road, but we are not yet sure we need to. With the powerful semantic representations offered by the latest deep nets, we find that smaller number of hand-annotated samples often suffice for the desired accuracy which makes the whole annotation process simpler and faster. Of course, if you have data & evidence to the contrary, we'd love to take a look.

Re: Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes

#40

Post by mNovak »

Looks great, good luck! Bonus points for XKCD. I'm a big fan of technologies that bring new capabilities to everyone, not just specialists of that field.

Minor clarification, I assume those 'includes X invocations' are per month, not a one time credit?

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