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There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: almost 3 years ago
Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: over 4 years ago
FWIW you can use roboflow models on-device as well. detect.roboflow.com is just a hosted version of our inference server (if you run the docker somewhere you can swap out that URL for localhost or wherever your self-hosted one is running). Behind the scenes it’s an http interface for our inference[1] Python package which you can run natively if your app is in Python as well. Pi inference is pretty slow (probably... - Source: Hacker News / 12 months ago
It’s an easy to use inference server for computer vision models. The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface). It’s backed by a bunch of component pieces: * a server (so you don’t have to reimplement things like image processing & prediction... - Source: Hacker News / almost 2 years ago
* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects. Source: over 2 years ago
For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com. - Source: Hacker News / over 2 years ago
Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers?ref=whoishiring1222 Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment. Over 100k engineers (including engineers from 2/3... - Source: Hacker News / over 2 years ago
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
TensorFlow Lite - Low-latency inference of on-device ML models
Apple Machine Learning Journal - A blog written by Apple engineers
Apple Core ML - Integrate a broad variety of ML model types into your app
Lobe - Visual tool for building custom deep learning models
Monitor ML - Real-time production monitoring of ML models, made simple.