Reading list: What infrastructure do you support for AI deployment?
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.

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