Enterprise Large‑Model Deployment and Data Governance: Insights from Deepexi’s President

The interview with Bai Haifeng, President of Deepexi’s product line, highlights that the current focus of large‑model deployment is on domain‑specific models, which require both a demand side seeking cost reduction and efficiency, and a supply side with mature training technologies.

He explains that building a traditional machine‑learning team is costly, requiring data engineers, BI engineers, analysts, data scientists, and algorithm engineers, making it difficult for many enterprises to assemble.

Large models lower technical barriers by allowing a single user with Copilot assistance to perform tasks previously needing a full data‑science team, while fine‑tuning typically follows a three‑step process: self‑supervised pre‑training, supervised fine‑tuning for specific tasks, and RLHF alignment.

Fine‑tuning reduces hallucinations and improves consistency, needing only a fraction of the original data. Effective data governance becomes crucial, shifting from traditional structured‑data governance to handling unstructured data required for model training.

Balancing dataset composition (e.g., 30% domain data, 70% general data) helps achieve flexibility, diversity, and accuracy while lowering overall data acquisition costs.

Different task types (representation vs. knowledge‑question) dictate fine‑tuning strategies: efficient methods like LoRA, QLoRA, and P‑tuning for representation tasks, and full‑parameter fine‑tuning for knowledge‑intensive tasks, which demand higher hardware resources.

Deepexi’s product system addresses efficiency, performance, and experience by offering the Fast5000E integrated training‑inference appliance, the FastAGI agent platform for rapid development of data and document agents, and the MQL query language to bridge large‑model reasoning with enterprise data‑lake workloads.

The overall approach reflects a cost‑economics perspective for domain large models, emphasizing agile data governance, tool‑driven pipelines, and a unified product suite to accelerate enterprise AI adoption.