ONNX has been around for a while, and it is becoming a successful intermediate format to move, often heavy, trained neural networks from one training tool to another (e.g., move between pyTorch and Tensorflow), or to deploy models in the cloud using the ONNX runtime. However, ONNX can be put to a much more versatile
ONNX has been around for a while, and it is becoming a successful intermediate format to move, often heavy, trained neural networks from one training tool to another (e.g., move between pyTorch and Tensorflow), or to deploy models in the cloud using the ONNX runtime. In these cases users often simply save a model to
Deploying trained AI models on edge devices might seem challenging. However, using minimal WebAssembly runtimes, and automatic conversion from ONNX to WebAssembly, modular AI/ML model deployment Over the Air (OtA) to pretty much any edge device is possible.
An uncommon combination allows efficient sequential learning on the edge.
With orthogonal persistence we can implement sequential learning on Edge devices
Reducing the memory footprint and improving the speed and portability of deployed models.
As more and more AI models are making their way to production—i.e, they are actually being used in day-to-day business processes—an active discussion has emerged questioning “how AI models should be deployed?” (e.g., using bloated containers? By rebuilding to stand-alone executables? By using “end-to-end” platforms with strong vendor lock in?) and “where AI models should
With Scailable, deploying complex AI models to the browser (and beyond) is surprisingly easy.
Using PyTorch, ONNX, WebAssembly, and the sclbl-webnode to deploy object recognition models directly in the browser.
Honestly? I don’t know. But I do think WebAssembly is a good target for ML/AI deployment (in the browser and beyond).
The shortest tutorial for deploying ML & AI models efficiently.
We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because of our deployment.