Scalability Engineering

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Scalability engineering focuses on building machine learning systems that continue to work well as demands grow. This growth can come from bigger datasets, more user requests, larger models, or new features added over time. A scalable system is designed so it doesn’t slow down or fall apart when the workload increases.

To achieve this, teams plan how models will be trained and deployed as they evolve and how the underlying infrastructure can expand without constant rework. The goal is to let different parts of the system grow independently so one bottleneck doesn’t block everything else. In ML-heavy environments, scalability also means being able to retrain and redeploy models often as data changes, while keeping the rest of the platform stable.

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