Eight layers constitute the complete enterprise artificial intelligence (AI) stack. At the base lies the infrastructure, encompassing the hardware and software required to run AI systems, followed by the data storage layer, which manages the vast quantities of information AI requires. The data preparation layer comes next, ensuring the data is in the right format and quality. Fourthly, the modelling layer uses this data to build the AI models.
The training layer is the fifth, where the AI models learn from the data. The sixth layer, inference, is where the AI model applies what it has learnt to new data. The human-AI interaction layer, seventh, is where humans interact with the AI, and finally, the AI application layer, the topmost layer, represents the visible applications of AI that end-users interact with.
Each layer is critical and requires a different set of skills and tools. While some companies can manage all eight layers, many will outsource some aspects to specialists. The stack’s complexity means that no single technology or tool can address all the needs, and hence, a mix of different technologies and vendors is often necessary.
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