The AI technology stack is a multi-layered framework of tools, libraries, platforms, and infrastructure required to build and deploy AI systems. It typically begins with data collection and preprocessing tools, followed by machine learning frameworks like TensorFlow, PyTorch, and scikit-learn. The stack also includes orchestration platforms such as Kubernetes, version control tools, and model deployment environments. At the top layer, APIs and user interfaces facilitate integration with applications. Cloud platforms like AWS, Azure, and GCP provide scalable resources for training and inference. Security, monitoring, and performance analytics are crucial components. A well-structured AI tech stack supports efficient development, scalability, and robust model performance.