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AI Model Deployment & MLOps › Scaling ML Infrastructure

How does Scaling ML Infrastructure work

Beginner

Answer

Teams add more servers, use distributed computing, or move to cloud based resources so that model training and predictions can handle increasing demand without slowing down.
Real-world example A growing app scales its ML infrastructure by moving from a single server to a distributed cloud setup to handle millions of daily predictions.

Common follow-ups: What is AI Model Deployment & MLOps, How is Scaling ML Infrastructure evaluated in practice, What tools are commonly used for Scaling ML Infrastructure

AI Model Deployment & MLOps topics: Introduction to MLOps Model Versioning CI/CD for Machine Learning
All 7 Scaling ML Infrastructure questions