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Introduction Getting a machine learning model to hit 95% accuracy in a local development environment is a massive achievement for a data scientist. But in the corporate world, an isolated model file sitting inside a Jupyter notebook is functionally useless. The true challenge begins when you try to deploy that model into a live cloud infrastructure, hook it up to real-time data feeds, and ensure it continues to deliver accurate predictions month after month. Deploying and managing production AI systems is notoriously difficult. Unlike traditional software development where the code behaves deterministically, machine learning systems are fundamentally dynamic. They depend heavily on three constantly shifting pillars: code, data, and infrastructure. When software fails, it usually crashes cleanly, leaving an error log. When a machine learning system fails, it often fails silently. The application remains up, the APIs respond perfectly, but the underlying predictions slowly degrade becaus...