How can I ensure my AI model scales efficiently when deployed across multiple cloud platforms?

Example Concept: Containerization involves packaging your AI model and its dependencies into a container, such as a Docker image. This allows the model to run consistently across different environments. Load balancing distributes incoming requests across multiple instances of your model, ensuring no single instance is overwhelmed. Cloud-native services, like Kubernetes, automate the deployment, scaling, and management of containerized applications, providing a robust infrastructure for scaling AI models efficiently.