GPU.LAND
Banana.dev
Apple Core ML
TensorFlow Lite
Clever Grid
Numba
Cython
Google CLOUD AUTOML
Scale Nucleus
Aquarium
PerceptiLabs
ML Image Classifier
Google Cloud TPUs
Roboflow Universe
Touchpoint Dashboard
Pretrained AI
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Based on our record, GPU.LAND should be more popular than Scale Nucleus. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I'm just going to mention here the experience of someone who ran gpu.land (doesn't exist any more). He did something similar, monetized it (very cheap) and then had to shut down because people were running crypto miners on it. I hope you have a plan to avoid that type of abuse. Source: about 2 years ago
RIP to gpu.land... I was hoping they would take off because they seemed to have a cool product with great pricing. Source: almost 3 years ago
There's also https://gpu.land (which has their own comparison page). Source: almost 3 years ago
Heya, I'm also so just keeping in touch. After liek 1 month of non redditing, someone replied who claimed to be the developer of gpu.land Apparently it is cloud computing for full Linux rather than the Jupyter notebook like what we tried before. Can I ask what is the update on the cloud computing site? I messaged the gpu.land person to see if we can get some free trial ($1 per hour on cheapest one but I don't know... Source: about 3 years ago
There are also more affordable GPU-for-DL-lending options like gpu.land, although I have never used them so I can't vouch for them -- just something I saw on PH. Source: about 3 years ago
At Scale we built a tool for model debugging in computer vision called Nucleus (scale.com/nucleus) designed exactly for this, which is free try out if you're curious to see where your model predictions are most at odds with your ground truth. Source: over 2 years ago
To address your point about gathering edge cases, which can also be defined as cases of low model fidelity for our use cases, there is active learning and tools such as Aquarium Learning and Scale Nucleus which make it easy to implement into workflows. Source: almost 3 years ago
Banana.dev - Banana provides inference hosting for ML models in three easy steps and a single line of code.
Aquarium - Improve ML models by improving datasets they’re trained on
Apple Core ML - Integrate a broad variety of ML model types into your app
PerceptiLabs - A tool to build your machine learning model at warp speed.
TensorFlow Lite - Low-latency inference of on-device ML models
ML Image Classifier - Quickly train custom machine learning models in your browser
