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Facing AI deployment challenges? Let’s share solutions!
Hey everyone! Jumping into AI deployment brings so much opportunity, but also unique challenges. From data quality to ensuring model accuracy or handling real-time processing, each step has its hurdles. For those building or scaling AI products, I’m curious: what’s been the toughest roadblock for you, and how did you navigate it?
At Sky Solution, we’re tackling some of these ourselves, and I'd love to hear insights or unexpected lessons from the community.
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Usama Saqib
my main issue so far has been generating interest in my product. I have recently launch an open source application that streamlines the usage of LLM for image generation but it has been difficult to find people who has use the app and who could provide feedback.
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This year I have made some attempts on AI products, but I often encounter a problem:
Each call to the AI model has a relatively high cost, but currently consumers’ willingness to pay is not particularly strong.
We often encounter great setbacks when exploring business models for AI products, because the trial and error cost of AI products is much higher than that of other types of products.
Oh, the joy of AI deployment! Who knew turning messy data into magic could be this tricky? Our biggest hurdle was real-time processing delays. We ended up rethinking our data pipeline, and voilà, smoother than butter! Speaking of AI, we're stoked about "Her Ideal Match" – ever wonder if AI can crack the dating code?