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So, pre-trained models and run-time models made off-the-shelf for IT folks to buy and maybe tune a little bit, will be necessary for AI to scale across enterprises and across the internet.” They basically have a comprehensive solution from the chip all the way to data centers at this point,” he says.
There is also a trade off in balancing a model’s interpretability and its performance. A deep dive into model interpretation as a theoretical concept and a high-level overview of Skater. There is often a need to verify the reasoning of such ML systems to hold algorithms accountable for the decisions predicted.
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Especially as they are often considered as working flawless and “off-the-shelf”, but things go wrong either way. One key take-away: If you want to have highest-quality software, you need expert developers that are capable of even diving into the underlying frameworks and libraries.
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