Enterprise Large‑Model Deployment: Data Governance, Fine‑Tuning Strategies, and Cost Economics

The interview with Ba Hai‑feng, President of Deepexi at DeepExi Technology, highlights that the current focus of large‑model deployment is on domain‑specific models, requiring both demand‑side cost‑efficiency and supply‑side mature training techniques.

He explains that traditional small‑model projects demand large, multidisciplinary data‑science teams, making talent costs prohibitive for many enterprises; large models, however, lower technical barriers by enabling a single user with AI copilots to accomplish tasks previously needing whole teams.

Training typically follows a three‑step pipeline: self‑supervised pre‑training, supervised fine‑tuning for a specific domain, and RLHF alignment to human values, exemplified by turning a generic Llama 2 13B into a specialized chatbot.

Data governance shifts from structured‑data focus to handling massive unstructured data, where high‑quality domain data acquisition is costly; instruction fine‑tuning, explanation tuning, and noise‑injection methods (e.g., Neftune) are used to improve model consistency and reduce hallucinations.

Balancing dataset composition (≈30% domain data, 70% generic data) improves flexibility, diversity, and accuracy while lowering overall data collection costs.

For different task types—representation‑heavy versus knowledge‑intensive—different fine‑tuning approaches are recommended: parameter‑efficient methods (LoRA, QLoRA, P‑tuning) for the former, and full‑parameter fine‑tuning (requiring high‑end GPUs) for the latter.

DeepExi’s product stack addresses these challenges: the Fast5000E training‑inference appliance provides affordable compute; the FastAGI platform offers agents (Data Agent, Doc Agent, Plugin Agent) to quickly build AI‑powered tools; and the MQL engine unifies heterogeneous data‑warehouse queries, enabling “Text‑to‑MQL” with near‑100% accuracy.

Overall, the company promotes an agile data‑governance model integrated with large‑model capabilities, emphasizing efficiency, performance, and user experience as the pillars of a sustainable cost‑economics framework for enterprise AI.