saintarian

8 ms·

1.
by saintarian 1y ago
Great project! Love the library+db approach. Some questions: 1. How much work is it to add bindings for new languages? 2. I know you provide conductor as a service. What are my options for workflow recovery if I don't have outbound net
2.
Nat Bullard's presentation on the state of Decarbonization(nathanielbullard.com)
1 points by saintarian 3y ago|0 comments
3.
Ontology-Oriented Software Development(blog.palantir.com)
2 points by saintarian 3y ago|1 comments
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Beyond model training: The untapped potential of AutoML platforms(nyckel.com)
4 points by saintarian 3y ago|0 comments
5.
by saintarian 3y ago
Part one is here: https://news.ycombinator.com/item?id=35765783
6.
Four questions concerning the internet, part two(paulkingsnorth.substack.com)
1 points by saintarian 3y ago|1 comments
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Object detection without bounding boxes(nyckel.com)
1 points by saintarian 3y ago|0 comments
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How to change the satellite imaging industry(joemorrison.substack.com)
2 points by saintarian 4y ago|0 comments
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Ways to use a data engine to improve your ML model(nyckel.com)
1 points by saintarian 4y ago|0 comments
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Service Oriented Design Applies to ML Too(nyckel.com)
4 points by saintarian 4y ago|0 comments
11.
by saintarian 4y ago
Shameless plug - for folks who don't want to take on the work of model selection, on-demand scaling of model serving, scaling the vector database for search set size and query throughput, we built a service that hides all this behind a
12.
by saintarian 4y ago
The easiest and likely most effective method may be to compute vector embeddings using a sentence transformer model, and find nearest neighbors among these vectors for all articles in the set. The distance between the nearest vectors will g
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Show HN: Semantic Search of Millions of NFTs(nyckel.com)
10 points by saintarian 4y ago|0 comments
14.
by saintarian 5y ago
Ha, I agree that software engineering is too hard for ML engineers, and even for software engineers like myself who have been doing it for 20 years like zcw100 said :). Author of the blog post here. It was definitely written from my narro
15.
by saintarian 5y ago
Author of the blog post here - it's very cool to see this on HN! I wrote this as someone who considers himself a half-decent software engineer trying to use ML for a side project and feeling frustrated by all the effort and "accid
16.
by saintarian 5y ago
Thanks for the input - that is useful to know.
17.
by saintarian 5y ago
Thank you! Everything just clicked when we saw that XKCD strip. Yes, you are right - 'includes X invocations' are per month.
18.
by saintarian 5y ago
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 ourselv
19.
by saintarian 5y ago
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?
20.
by saintarian 5y ago
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 muc
21.
by saintarian 5y ago
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
22.
by saintarian 5y ago
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 deve
23.
by saintarian 5y ago
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 di
24.
by saintarian 5y ago
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 th
25.
by saintarian 5y ago
Ha - that one makes us chuckle too! But we can't promise to not remove it.
26.
by saintarian 5y ago
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&quo
27.
Launch HN: Nyckel (YC W22) – Train and deploy ML classifiers in minutes
103 points by saintarian 5y ago|48 comments