Practices and Reflections on Building an AI Platform at Zhongyuan Bank

The article presents the background, goals, and development timeline of Zhongyuan Bank's AI platform, emphasizing the need for a unified, resource‑efficient environment that supports the entire machine‑learning lifecycle.

Guided by MLOps principles, the platform aims to automate and standardize model development, deployment, and operations, addressing challenges such as fragmented resource allocation, inconsistent development environments, and limited collaboration between data scientists, engineers, and business users.

Key functional modules include:

Data ingestion and management, offering multiple import methods, metadata handling, and exploratory analysis.

Data processing and feature engineering with visual operators, reusable pipelines, and support for both batch and streaming data.

Model development via three approaches: code‑first for data scientists, workflow‑based drag‑and‑drop for engineers, and automated modeling for business users.

Model training with hyper‑parameter tuning, resource request, real‑time monitoring, and log inspection.

Model evaluation providing ROC, AUC, accuracy, recall, specificity, multi‑dimensional visual comparisons, and automated scoring.

Model publishing through a centralized model repository, supporting one‑click deployment, SDK export, and various release strategies (gray, full, shadow).

Model serving with Java or Python runtimes, scalable inference clusters, and continuous monitoring of performance and resource usage.

Infrastructure is orchestrated by Kubernetes for unified compute and storage scheduling, with Docker containers delivering customizable runtime environments, enabling isolation via namespaces and efficient resource utilization.

The platform also incorporates ModelOps practices to manage the full model lifecycle, including process management, agile deployment, asset governance, and monitoring/alerting, complemented by organizational structures and regulatory policies to ensure compliance and risk control.

Overall, the AI platform integrates AI, data, and compute layers to provide a comprehensive, production‑grade solution for banking AI applications.