How does infrastructure impact the deployment and performance of ML models?

Infrastructure significantly influences the deployment and performance of ML models by affecting latency, scalability, and reliability. Choices between cloud, on-premises, or edge computing can determine how quickly models respond, especially under varying loads. Resource allocation (CPU, GPU) affects processing speed and throughput. Additionally, network bandwidth impacts data transfer speeds and, consequently, inference times. Proper infrastructure planning ensures models perform optimally and can handle predicted traffic.