China’s First AI Model Development Standard – Highlights from the AI Engineering Forum
The AI Engineering Online Forum, co‑hosted by China Academy of Information and Communications Technology, unveiled the industry’s first AI Model Development and Management (Model/MLOps) maturity standard, featured expert insights from finance, telecom, and tech leaders, and showcased practical MLOps implementations across banking, Huawei, and AI startups.
On April 28, 2022, the China Academy of Information and Communications Technology (CAICT) Cloud Computing and Big Data Institute, together with the AI Engineering Promotion Committee and the Efficient Operations Community, hosted the “AI Engineering Online Forum,” attracting over 2,000 live participants.
The forum officially released the industry’s first “Artificial Intelligence‑Driven R&D Operations Integration (Model/MLOps) Capability Maturity Model – Part 1: Development Management” standard, summarizing years of work with more than 80 participating enterprises.
Keynote by Wei Kai
Wei Kai, Deputy Director of the CAICT Cloud Big Data Institute, presented the background, framework, and objectives of the standard, highlighting pain points such as incomplete toolchains, immature operations, and high adoption barriers, and emphasizing how standardized top‑level design can accelerate large‑scale AI project deployment.
In‑depth Interpretation by Qin Sisi
Qin Sisi, Project Lead of the AI Department and head of the R&D Operations Working Group, explained the standard’s structure, testing, and evaluation methods, covering the full lifecycle from data processing to model deployment and service support.
Standard Scope
The model‑development, deployment, and operations workflow is divided into multiple dimensions, specifying functional and performance metrics for development platforms. The “assessment‑driven improvement” approach enables enterprises to benchmark, identify gaps, and continuously enhance their AI R&D‑Ops integration.
Speaker Highlights
Peng Hongjian (Zhongyuan Bank) – ModelOps practice in banking scenarios.
Ju Xijian (Huawei Terminal BG) – MLOps implementation in terminal cloud.
Yuan Jinhui (Yili Technology) – Core value and democratization path of AI.
Liang Wanqiang (Efficient Operations Community) – Google’s machine‑learning engineering practices.
All speakers shared practical experiences and insights, illustrating how AI engineering is becoming a core focus for enterprises.
For those who missed the live session, the forum’s recordings and selected PPTs are available via the provided links.
Efficient Ops
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