⚡ Hidden Challenges of Deploying AI in the Real World

Many AI projects never make it past the proof-of-concept stage. Why? Because real-world deployment comes with hidden challenges that companies underestimate.

The AI Deployment Gap

🔴 Common Challenges

1. Data Drift & Model Decay – AI models degrade over time without continuous monitoring.
2. Scalability Issues – Prototypes work in controlled environments, but real-world data is messy.
3. MLOps & Infrastructure – Most companies lack end-to-end AI pipelines.

How to Overcome Deployment Challenges

Final Thoughts

AI deployment is more than just training models—it requires scalable architecture, governance, and operational excellence.

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