Why Your Perfect AI Model Fails in Production (And How to Fix It)

🚀 Your AI model works perfectly in Jupyter notebooks. Then you deploy it and everything breaks.
I learned this the hard way when my "perfect" model crashed our servers on day one. There's a massive gap between demo magic and production reality - and that's where the real learning happens.

In this video, you'll discover:
✅ Why models that work in development fail in production
✅ The unglamorous challenges that actually matter
✅ What separates notebook demos from scalable systems
✅ How to think beyond accuracy metrics
✅ The monitoring strategies that catch problems early

The Production Reality:
While everyone debates which model architecture is "best," there's a group of practitioners quietly shipping AI systems that handle millions of real users. They're solving the problems that matter: inference costs, latency issues, mysterious performance drops, and scaling nightmares.
Learn from the pros: I've been following engineers like Paul Iusztin and Miguel Pedrido who've built production ML systems that actually work at scale. Their insights usually stay locked in private engineering channels - until now.

💬 What's your biggest production AI challenge?

🔥 Inference costs spiraling
⚡ Latency killing UX
🐛 Mysterious model drift
💸 Resource scaling nightmares

Drop your war stories in the comments - this community's collective experience is our greatest teacher.

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#ProductionAI #MachineLearning #MLOps #AIEngineering #TechReality #Doug Ortiz

ProductionAI,MachineLearning,MLOps ,AIEngineering,TechReality, Doug Ortiz
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➡ LinkedIn:   / doug-ortiz-illustris  
✍️ Blog: https://dougortiz.blogspot.com/

▬▬▬▬▬▬ ⏱ Timestamps ▬▬▬▬▬▬
00:00 Why Your Perfect AI Model Fails in Production (And How to Fix It)
00:12 Deploying Models in the real-world
00:46 What Truly Matters
01:05 Let's Learn together
01:19 DeepSeek In Production Online Event