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Veröffentlicht : Nov 20, 2019
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Nov 2019
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

The tools and frameworks ecosystem around neural networks have been evolving rapidly. The interoperability between them, however, has been a challenge. It's not uncommon in the ML industry to quickly prototype and train the model in one tool and then deploy it in a different tool for inference. Because the internal format of these tools aren't compatible, we need to implement and maintain messy convertors to make the models compatible. The Open Neural Network Exchange format ONNX addresses this problem. In ONNX, the neural networks are represented as graphs using standard operator specifications, and together with a serialization format for trained weights, neural network models can be transferred from one tool to another. This opens up lots of possibilities, including Model Zoo, a collection of pretrained models in ONNX format.

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