How to Build an Enterprise-Grade AI Middle Platform: Solutions for High Data Security Needs

Build an AI middle platform to support knowledge base management, data collection, annotation, model training, and storage for enterprise AI transformation.

Welcome to the "Practical Application of AI Large Language Model Systems" Series

Table of Contents

Previously, we discussed the need for a platform to support both traditional small models and large models. To make these models usable, we need an engineering platform. For instance, with RAG, we need to manage the knowledge base and maintain the vector database. Developers can't handle everything through APIs, so we need a unified technical platform. This platform should support AI transformation in businesses and be flexible enough to be used across different industries. We call this the AI Middle Platform.

Before the explosion of large models, many big companies already had mature small model development platforms, like Baidu's EasyDL and Alibaba Cloud's PAI.

With the rise of large models, companies launched platforms specifically for them, such as Baidu's BML and Alibaba Cloud's BaiLian and Lingji. Open-source platforms for large language models, like Dify and LangChain, also emerged.

Some companies can use AI platform services from cloud providers without developing their own. This is cost-effective and quick. However, companies with high data security needs might prefer private deployment or full in-house development. Private deployment might not allow full customization unless they pay substantial fees, leading many to develop their own platforms for greater flexibility.

This lesson covers how businesses can build their own AI Middle Platform and the key considerations.