HN Explorer

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

103 points by saintarian ↗ HN
Hi HN, we’re George, Oscar, and Dan, the founders of Nyckel (https://nyckel.com). Nyckel allows developers with no ML experience to train and deploy ML functions to classify images and text with very little training data. We let you go from a few labeled data points to a serverless machine-learned classifier in minutes.

The ML-as-a-Service space is dense, including some recent YC companies; so why did we create Nyckel? Our goal is to create a tool that is light, fast, fun, and accessible. Training only takes seconds, you only need 10s of annotations, we avoid ML-lingo and abstract away concepts that make developers feel like outsiders. Our pricing is transparent, signup is instant, and the platform is 100% self-serve.

Dan, an experienced engineer without any ML background, was building a social website that required manual curation of user-contributed content. He looked into automating this curation with ML and found offerings that required complicated setup and knowledge of ML concepts. He talked to his AI-researcher friend Oscar, and together they realized that the current solutions were unnecessarily complex and didn’t expose the right developer-friendly abstractions. We think there are many engineers like Dan who leave similar problems unsolved because of the effort required.

Using Nyckel, you upload a small (or large) amount of data, annotate a minimum of 2 examples per class, and have a trained model deployed in the cloud and callable via a REST API. All of this happens in seconds. As you use the model, you can continue to improve it by providing more data points as you encounter them. You can also explore and annotate your data in the UI.

The Nyckel AutoML engine is based on meta transfer learning. It’s “transfer” because it leverages a large set of pre-trained neural networks to represent your data, and it’s “meta” because we make informed decisions on which of the networks to try based on your particular problem. The design allows for a highly parallel execution where features are extracted and models trained by 100s of compute nodes in parallel requiring only 10s of seconds to train even with 1,000s of samples. We keep abreast of the latest deep-learning networks and add new networks to the system to improve existing and new models. Your trained model is immediately deployed on an elastic inference infrastructure.

Our customers are using us to do things like: tag and organize photos in a used-car marketplace; triage customer responses and support tickets for CRM (in multiple languages); determine fake vs real profile pictures to help with user verification; analyze blood sugar charts to suggest corrective actions; and build a barcode-less scanner for bulk foods.

Oscar has over 4k scientific citations of his AI research, as well as several industry applications behind him. Dan has designed multiple developer APIs throughout his career, most recently Square’s developer APIs. I led the Functions-as-a-Service team at Oracle Cloud and have extensive experience building large cloud systems. We think that ML, cloud, and API expertise is the right combination for this problem!

We have elastic pricing with an always-free tier. Beyond the free tier, we make money when you invoke your function to make a prediction.

We're really happy we get to show this to you all. Thank you for reading it all! We’d love for you to check us out, and share your thoughts on Nyckel and your experiences in the ML tooling space in general.

49 comments

[ 4.7 ms ] story [ 106 ms ] thread
Cyril_HN
Would this let a complete novice tag and classify words, say, to only highlight verbs? Would that task be easier than typical methods?
beijbom
Hi Cyril_HN! Thanks for your question. What you are asking for is sometimes called "part of speech" tagging. We currently don't support that but will add it down the road along with more advanced image outputs like detection.
montenegrohugo
Heyo, cool product. I know setting up and deploying ML on your own is a big PITA from own experience, so ML as a service is a no brainer.

Questions: What do you guys see as the long term vision here? Where would you like to go, if you could? Obviously the value prop is simple and clear right now, and you have tons of competition. But assuming you're profitable, get traction, what would you LIKE to be in 5-10 years? What's your dream as entrepreneurs?

Best of luck. Also, for some small feedback, the UI currently looks a bit generic, bootstrap-y. I'd recommend giving it a small facelift if you can.

saintarian
We think the number of use-cases for ML is going to grow drastically as 1) ML state-of-the-art continues to get better; and 2) Developers realize how accessible it can be. More problems will be solved by a "machine-learned function" rather than imperatively coded functions. Taking it one step at a time, in 5-10 years we'd love to be the default place the developers go to solve their ML problems. This would mean that we go beyond image/text classification to a broader set of input and output types.

Thanks for the feedback on the UI! None of us are UI experts and we agree that it isn't great. We're working to make it better.

danott
Haha yea I'm to blame for the crappy UI... I believe in principle with shipping things you're uncomfortable with, but it also hurts my soul.
isusmelj
Hi George, Oscar and Dan. Congrats on the launch! Nyckel sounds super interesting. It looks like you mostly focus on classification tasks. Any plans to also support other tasks such as object detection or image segmentation?
danott
Thanks. They're both on the roadmap; in fact we've got a handful of users in a private beta for object detection; let me know if you're interested in getting in on that!
Melting_Harps
I'd like to hear more how I could contribute to the beta, I'm studying AI and ML after a career in fintech and would like to see how things are done to train these models first hand.

I'm playing with Nykel right now and it looks pretty straightforward with a clear and simple UI.

beijbom
Thanks! I'd love to chat more about your use-case and how we can help you. Drop me a line at oscar at nyckel dot com to set something up.
[deleted]
(comment deleted)
blululu
This is very cool. A few quick questions:

1.) Would it be possible to buy the model and integrate/host it on my own machines?

2.) Would you consider making solutions for embedded ML in the future?

danott
We do think "model export" is important, but we're still getting our heads around how to do it in the most non-ML-expert friendly way. We don't think the persona we're building for wants a weights file dropped in their lap. What output / format would be ideal from your perspective?
isaacimagine
Maybe a cross-language library that takes a binary weights file (with embedded model information) and exposes an interface similar to that of the web API? Or a local lightweight version of Nyckel that one can run on their own infrastructure (that exposes the same REST API)?

Just spitballing here; these two would be the most convinent for the use-cases I have in mind.

saintarian
Agreed that they would be convenient. We are looking at both those options. There are devils in the details like seamlessly taking advantage of available hardware acceleration.

Would love to talk more about your use case so we prioritize the right things for model export. Drop me a line (george at nyckel dot com).

blululu
I was thinking of something like an ONNX file or something that can easily slot into different runtimes.

Makes sense that this would be less beginner friendly so maybe you're correct that this is a P2 feature.

I guess I was thinking more in terms of pricing models and scaling up a service which is obviously a complicated decision for a startup so I'm not really sure what makes sense here. My rationale for wanting to buy/rent the model is that as a service scales it becomes increasingly important to own the model and the hosting. One of my concerns with building on top of a service like this is that it will potentially reach a chokepoint in the future. In general training a model is expensive and unique but hosting it is a commodity service. This will incentivize customers to use the service when they are small and then drop it when they grow to a certain size which is not necessarily ideal for either party.

danott
Yep. The pricing model does basically break down for model export, but I think there's a solution there. Or, said another way, if we could make it really easy to do then there's an adjacent business we could move into.

In terms of keeping customers as they grow, our view (hope?) is that these models will be continually updated because of new annotations on their end, and from new training techniques on ours. And that concept of continuous improvement will push people toward a SaaS model.

When you say chokepoint, are you referring to cost, or latency, or something else?

PolemicThoughts
As a SWE, my preferred export format would be a folder with a Dockerfile. I'm not sure how GPUs work with docker, but if I could just run the container on a machine with a gpu, or deploy it into a k8s cluster with an affinity for a node with a gpu, that would be my ideal. I'm not sure how I'd want model drift to be addressed, so being able to pull down and deploy new versions would be something to consider as well.
saintarian
Thanks for the input - that is useful to know.
petters
Great to see alumni from Lund here! Oscar Beijbom created the ML tech for the airbag bicycle helmet Hövding right after college.
beijbom
Haha, that's right. Thanks for the shout-out @petters!
zschuessler
I couldn't stop chuckling over the example use cases' screenshot for Sentiment Analysis. Please don't remove it, it made my day and it's only 10am here.
danott
Oh man, we literally just got new images from our new designer and I think they went a different direction with that one :)
saintarian
Ha - that one makes us chuckle too! But we can't promise to not remove it.
dangledangle
I'm curious to know how this is different from Microsoft's CustomVision and other such tools. In fact, they've been around for much longer with the exact same product.

Thanks.

danott
Well there’s a continuum of offerings, some having lots of custom control of the training pipeline and on the other side things like CustomVision that try to make it easy / hide the complexity. We consider ourselves even further to the “hide complexity” side, since we try every domain automatically vs. making you choose, re-train automatically, etc. Initial feedback from our users is that even recall vs. precision is something they don't want to have to think about. In addition, we don't limit ourselves to only vision - we'd like to be the one stop shop for ML as a service.
colincooke
Interesting product strategy, but I can't help laugh at some of the product examples like "barcodeless scanner" or "quality inspection" (I can only comment on imaging as that's my background). First of all the idea of replacing a perfectly functional barcode scanner with an ML model is not a great sell (having how much I pay at the register dependent on the lighting at the grocery store is not going to be a fun time). Second, ML models are great when failure is OK at a specific rate, none of the image classification examples shown have that characteristic.

While I'm sure the founders are competent and understand these limitations, it's unfortunate that they've chosen such flawed examples to show off on their home page.

danott
Actually, all these examples are straight from our userbase. I can't speak to how successful they're being in their own businesses, but they seem happy with the ML. The 'barcodeless scanner' in particular is about scanning bulk foods that don't have barcodes, and it seems to work for them.
octodog
Barcodeless scanner is a great example, this is something that major supermarkets are already rolling out as a complement to their self-serve checkouts.
gamerDude
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 bunch of things that I don't really know what they are. I was a statistician before ml became popular, so I understand the underlying premises, but none of the modern language.

I would also really have liked to been able to filter by say, if the confidence level is over 80%, how accurate is the model. Because then I can say, well, if we use this, I can knock out tons of work at the 80% confidence rate and then just manually work with the rest.

I'm also not sure if you are seperating training/test data. All in all, looks nice, it was very easy to get started, but I'm a bit lost on what to do next and I'm having trouble judging how useful this will be to me and if I should invest more time.

saintarian
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, Sort By = Least Confident Prediction.

Your idea us helping you pick a confidence threshold is a good one. We'll get that into our near-term roadmap.

We use a technique called cross-validation to seperate training and test data. We have that documented here: https://www.nyckel.com/docs#cross-validation

gamerDude
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?

saintarian
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.

danott
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.

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
saintarian
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).
rubyskills
I think you're going to have a hard time using historical data to deploy a real time algo trader, unless your strategy involves only trading on a macro scale or against some time horizon. One hour and 30 minutes is pretty common as you typically see a flurry of volume at these intervals. 30 minutes also has some legacy pit trader strategies that I'm sure automated systems use. Arbitrary time slices of price or volume data doesn't really dictate how the market works in real time however. Hmmm, now that I think about it this might be viable, but execution is an entirely different beast in production depending on which instrument you're trading. Have to consider current volatility, slippage not to mention all of the crazy stuff that happens at the liquidity level... And even then things get spoofed all the time. Then you start dealing with the HFT stuff.
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?

saintarian
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.

beijbom
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.

rexreed
In what ways is this different from some of the cloud based AutoML tools such as Google Cloud Vision AutoML? Or tools like Roboflow?
saintarian
There are a continuum of offerings in this space. Some have lots of custom control of the training pipeline and deployment, and on the other side, things like RoboFlow that try to make it easy / hide the complexity. We consider ourselves even further to the “hide complexity” side, since we try several deep networks automatically vs. making you choose, re-train automatically, abstract-away non-essential ML jargon, etc. In addition, we don't limit ourselves to only vision - we'd like to be the one stop shop for ML as a service. We also have developer-friendly pricing with quick and easy signup.

We benchmarked ourselves against Google AutoML and HuggingFace, looking at both user experience and model performance, and wrote it up in a blog post that may interest you: https://www.nyckel.com/blog/automl-benchmark-nyckel-google-h...

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?

saintarian
Thank you! Everything just clicked when we saw that XKCD strip.

Yes, you are right - 'includes X invocations' are per month.

alexcnwy
Very cool, well done! I love the training workflow. Is it possible to download trained models to use them offline (e.g. in Enterprise package maybe)?
saintarian
Thank you! We do think that "model export" is important, but we're still working out how to do it in the most seamless and non-ML-expert friendly way. Do you have a use-case and target hardware in mind?
xiekomb
Hi, exactly what I am looking for. Do you handle multiple languages for text categorization (other than English)?
danott
Yes! We have several language-specific models we train against to see which performs the best for your data. Which language (or languages) do you need? (feel free to email me at dan at nyckel dot com)