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

Introduction – The current focus of large‑model deployment is on domain‑specific models, requiring demand‑side cost‑reduction and efficiency gains, and a supply‑side with mature training technologies.

Expert Insight – Ba Hai‑feng, President of Deepexi product line at DeepExi, explains that traditional machine‑learning teams demand many specialized roles (data engineers, BI engineers, analysts, data scientists, algorithm engineers), making talent costs prohibitive for most enterprises.

He notes that while small models have lower compute and data requirements, achieving good performance remains complex due to fragmented tools and diverse algorithms.

Large models lower technical barriers by shortening the data‑to‑output pipeline, allowing a single user with AI copilots to replace large data‑science teams.

Training typically follows three steps: self‑supervised pre‑training, supervised fine‑tuning for specific tasks, and RLHF alignment to human values.

Fine‑tuning transforms a generic model (e.g., Llama 2 13B) into a specialized chatbot (Llama 2 13B‑chat) with a fraction of the original data.

Fine‑tuning reduces hallucinations and improves consistency and professionalism, requiring only 0.01%–0.1% of the data used for pre‑training.

Efficiency Construction: Data as the Way, Model as the Technique – Effective data governance is the core variable, distinct from traditional big‑data governance, focusing on unstructured data needed for model training.

Unstructured data governance faces high costs for acquiring high‑quality domain data.

Instruction fine‑tuning (e.g., ChatGPT, Llama 2) uses prompt‑response pairs generated by stronger models or humans; explanation tuning further enhances data quality by decomposing answers step‑by‑step.

Techniques like Neftune add noise to data for robustness.

AI models can also compress unstructured data into structured knowledge using LLMs such as Claude 2, GPT‑4, or locally deployed Llama 2 and ChatGLM2 for security‑sensitive environments.

In sectors like petrochemicals, multimodal data (images, seismic, video, IoT) must be processed by small and large models to extract explicit and implicit knowledge for model training.

Dataset Balance: Accuracy vs. Diversity – High‑quality domain datasets (≈30% of training data) combined with general data (≈70%) achieve a balance that reduces overall data acquisition cost while maintaining flexibility, diversity, and accuracy.

Data Types: Another Dimension – Tasks split into representation‑oriented (e.g., re‑phrasing Java threads) and knowledge‑oriented (e.g., credit‑limit definitions). Knowledge‑oriented tasks often require full‑parameter fine‑tuning, demanding high‑end hardware (e.g., 80 GB A800 for Llama 2 13B).

Statistical analysis tasks (high accuracy) sit at the base, while predictive tasks tolerate lower accuracy.

Product System: Balancing Efficiency, Performance, and Experience – DeepExi offers the Fast5000E all‑in‑one training‑inference machine for enterprises lacking compute resources, and the FastAGI agent platform for rapid development of AI agents (Data Agent, Doc Agent, Plugin Agent, etc.).

They advocate a shift from traditional top‑down data governance to agile, tool‑driven governance, integrating data development and governance to improve quality, breadth, and efficiency, especially for unstructured data.

Cost Economics of Domain Models – By evaluating data quality across accuracy, diversity, statistical and predictive dimensions, DeepExi guides low‑cost, high‑efficiency training of domain models, aligning product planning with economic principles for sustainable AI adoption.