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Stellar inference speed via AutoNAS
Practical AI: Machine Learning, Data Science, LLM
The Output Path of a Deployment Model?
When you're going through the training process, how are you accounting for kind of that variability out there? You know, if you're not targeting the hardware in the architecture search that you're going to deploy to, how do you all account for that? How do you say, ah, there's a very unique configuration for my output target, my deployment target. What does that look like from a practitioner perspective? Out of the theory and into the hands on? So we prefer to connect to the actual hardwerd that the model is going to be deployed on. In our perspective, measuring the metric that really matters on the actual device is the way to go.
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