Unlocking AI Success: A Deep Dive into the Model/MLOps Capability Maturity Framework

Background

To achieve orderly and efficient development and operation of AI assets such as data, algorithms, and models during AI project development, the industry is actively building MLOps management systems to enable large‑scale AI deployment. The China Academy of Information and Communications Technology (CAICT) Cloud Computing and Big Data Institute and the AI Engineering Promotion Committee, together with over 30 industry partners, have completed the world’s first AI model development management standard— Model/MLOps Capability Maturity Model Part 1: Development Management . The formal document and assessment plan are finished, and the first round of assessments is underway.

Evaluation Value

The Model/MLOps capability maturity assessment is intended for organizations undergoing AI transformation or upgrade, focusing on machine‑learning model development projects. It aims for production‑scale, high‑quality, low‑risk AI models by advancing capabilities in development management and model delivery. Based on the “Development Management” standard, the assessment concentrates on the machine‑learning project development process, helping enterprises locate their capability level, diagnose gaps, and receive customized improvement paths, thereby promoting organizational AI engineering and trustworthy AI governance.

The assessment incorporates core MLOps principles such as automation, version control, experiment tracking, testing, and reproducibility .

Maturity Assessment Overview

The “Development Management” standard comprises three capability sub‑domains—Requirement Management, Data Engineering, and Model Development—covering 10 capability items, 28 sub‑items, and nearly 240 detailed requirements.

This article is the first in a series interpreting the “Development Management” standard; future articles will continue with the Data Engineering and Model Development sub‑domains.

Requirement Management Domain

To address chaotic requirement processes, inconsistent understanding among roles, and uncontrolled risks in machine‑learning projects, strengthening requirement management improves project success rates and reduces negative impacts of requirement changes.

Requirement Management is the first stage of ML project development, covering requirement analysis, test case design, and project planning. The sub‑domain’s overall grading criteria are shown below:

1. Demand Analysis

Many ML projects fail to meet real business needs or require rework because of insufficient demand analysis before model development.

Demand analysis helps developers identify requirement validity, assess implementation difficulty, and formulate ML modeling solutions. It includes three sub‑items: demand confirmation, feasibility assessment, and scenario design.

Demand Confirmation: Technical staff fully understand the business goal and requirements.

Feasibility Assessment: Evaluate business value and feasibility.

Scenario Design: Translate business needs into AI problem descriptions and formulate model strategies.

2. Test Case Design

The purpose of test case design is to ensure delivery and acceptance quality, encourage early business test case writing, reduce requirement change risks, and align business and technical understanding.

Based on demand analysis and scenario design, business acceptance test cases are written and acceptance criteria defined. Test case design includes two sub‑items: test case writing and test case management.

Test Case Writing: Write business test cases and define acceptance standards according to the scenario design.

Test Case Management: Manage and track the lifecycle of test cases, including publisher, content, status, priority, and test data.

3. Project Planning

Machine‑learning projects involve many roles, complex processes, and high resource consumption. Effective project planning reduces uncertainty, tracks progress and risks, and improves development efficiency and delivery quality.

Project planning includes three sub‑items: schedule management, resource management, and risk management.

Schedule Management: Define and manage the development schedule, arranging time nodes.

Resource Management: Plan human, data, software, and hardware resources required at each stage.

Risk Management: Identify and manage risks related to personnel, schedule, cost, scope, etc., and devise mitigation strategies.

Final Note

After this round of assessment, the first‑issue Model/MLOps Capability Maturity Model – Part 1: Development Management certification will be released. Registration closes on June 20, with centralized assessment in June. The second part of the standard, “Model Delivery,” is already under development, and more industry experts are welcome to join.