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كتابات حول بناء أنظمة ذكاء اصطناعي تعمل بموثوقية في الإنتاج: ما الذي ينكسر، وما الذي يصمد، وكيف تميّز بينهما قبل أن تنفق ميزانية.
المقال متوفر بالإنجليزية
7 مقالات
- 2026-06-29 · MLOpsWhy your vision model gets worse over timeA detector that worked on launch day can quietly degrade for months before anyone notices. The model didn't change — the world did. Here's what drift looks like on a real camera, and how to catch it before it costs you.
- 2026-06-29 · تحديد النطاقWhen computer vision is the wrong toolVision is the right answer less often than the hype suggests. A few honest tests for whether your problem actually needs a camera and a model — before you spend a budget finding out it didn't.
- 2026-06-29 · تحديد النطاقWhat a computer-vision proof of concept should actually proveA good PoC isn't a demo that works once on a clean clip. It's a cheap, fast answer to one question: will this survive your real conditions? Here's how to scope one so it tells you something true.
- 2026-06-28 · الرؤية الحاسوبيةYour cameras are already a sensor networkMost industrial sites have spent years installing CCTV for security. The same feeds can answer operational questions too — if you treat the camera as a sensor, not just a recorder.
- 2026-06-25 · الفيديو الآنيLatency budgets: the number that decides your video pipelineBefore you pick a model, pick a latency budget. How many milliseconds you have per frame quietly determines almost every other decision in a real-time vision system.
- 2026-06-20 · MLOpsWhy computer vision fails in production (and how to ship it anyway)A model that scores 95% on your test set can still be useless on a live camera feed. The gap between a notebook and a running system is where most CV projects quietly die.
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