Quantization is a process used in machine learning and artificial intelligence to reduce the precision of model parameters, thereby optimizing models for efficient deployment. By converting floating-point weights and activations to lower precision formats, such as integer representations, quantization decreases the memory footprint and enhances the computational speed of neural networks. This is especially beneficial for deploying AI models on resource-constrained devices, such as mobile phones or embedded systems, where performance and efficiency are critical. While quantization can lead to minor losses in accuracy, techniques like post-training quantization and quantization-aware training can help mitigate these effects, allowing for more efficient and practical AI applications.