Example Concept: Scaling AI models on cloud platforms involves using cloud-native services like auto-scaling groups, load balancers, and managed machine learning services. These tools allow your model to handle increased loads by automatically adjusting resources based on demand. Additionally, using containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes) can help in managing deployments efficiently across multiple instances.