For AI deployment, businesses need infrastructure that supports computational power, storage, and reliable networks. Key components include Cloud Infrastructure: Platforms like AWS, Google Cloud, and Azure offer scalable resources (GPU/TPU), essential for AI model training and deployment. On-Premises Infrastructure: For businesses with security or regulatory needs, high-performance servers and local storage can be used for AI models. Hybrid Infrastructure: Combines cloud and on-premises solutions for flexibility, keeping sensitive data on-site while leveraging the cloud for scalability. Edge Computing: For IoT applications, AI models can be deployed on local devices to reduce latency and enable faster decision-making. Data Storage: AI requires vast data storage (e.g., databases, data lakes) for training and quick access. Networking: A fast network ensures smooth communication between AI models and other systems. Security Infrastructure: Protects data and models through encryption, access control, and monitoring. This infrastructure ensures efficient, secure, and scalable artificial intelligence deployment.