AI infrastructure refers to the underlying framework that supports and enables Artificial Intelligence (AI) systems to operate effectively. It encompasses a range of components, including hardware, software, data storage, networking, security measures, and management tools. The purpose of AI infrastructure is to provide the necessary resources for training, deploying, and managing AI models.

Key Features of AI Infrastructure

AI infrastructure typically consists of several key features that enable efficient operation:

• Data Storage**: Efficient data storage solutions are essential for housing vast amounts of data used in AI Platform model development. This includes databases, object stores, and file systems.
• Processing Power**: High-performance computing hardware, such as graphics processing units (GPUs) and tensor processing units (TPUs), is necessary to handle complex calculations required by AI models.
• Network Connectivity**: Robust network infrastructure ensures the smooth transfer of data between components within an AI system. This includes high-bandwidth storage networks, in-memory computing fabrics, and specialized networking protocols like Open Compute Project’s (OCP) Interconnect Fabric.
• Security Measures**: Implementing robust security measures is critical to safeguard against unauthorized access or tampering with sensitive data and models. These include encryption techniques, secure authentication processes, and anomaly detection tools.