AI Deployment Integrity: Ensuring Correct Behavior
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In this thrilling IBM Technology segment, the team delves into the critical task of keeping AI in check. Picture this: data scientists and AI engineers crafting models in a development space akin to a sandbox - a place of creation and perfection. But the real challenge comes when these models are unleashed into the wild, known as the production space. How do we ensure they don't go off the rails like a runaway train?
Well, fear not, for the team lays out three ingenious methods to maintain AI sanity. Firstly, by comparing the model's output to ground truth, they can swiftly spot any deviations from the desired path. Secondly, a clever comparison between deployment and development outputs acts as a beacon, guiding them back on course. And let's not forget the nifty use of flags and filters to sift through the AI's output like a seasoned detective, weeding out any unwanted surprises.
It's a high-stakes game of precision and vigilance, where even the slightest deviation can spell disaster. But armed with these three powerful methods, the team stands ready to tackle any challenges that come their way. So buckle up, folks, as we embark on this exhilarating journey into the world of AI integrity and control.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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