ALPACA构建自适应AI流水线，支持多类用户全流程开发。
ALPACA -- Adaptive Learning Pipeline for Comprehensive AI
- 融合可视化与代码开发，覆盖数据到部署全阶段
- 基于Celery+Redis任务调度，支持云端弹性扩展
- 适合非专家用户，助力AI落地日常应用
人工智能技术的快速发展使AI流程复杂化，涵盖数据收集、预处理、训练、评估和可视化等多个环节。为向不同用户群体（如专家、跨领域专业人士及普通用户）提供高效易用的AI解决方案，系统设计需兼顾易用性与可信度。本文提出ALPACA（面向高级综合人工智能分析的自适应学习流水线），集成可视化与代码化双模式开发，支持全流程AI流程管理。其架构采用Celery（基于Redis后端）实现高效任务调度，使用MongoDB进行无缝数据存储，并通过Kubernetes实现云端可扩展性与资源优化。未来版本将引入联邦学习、持续学习及可解释AI技术，进一步提升安全性、可用性与可信度。系统通过一个用于相似性识别的Android应用进行演示，凸显其在日常生活中的应用潜力。
原文摘要 · Abstract (English)
The advancement of AI technologies has greatly increased the complexity of AI pipelines as they include many stages such as data collection, pre-processing, training, evaluation and visualisation. To provide effective and accessible AI solutions, it is important to design pipelines for different user groups such as experts, professionals from different fields and laypeople. Ease of use and trust play a central role in the acceptance of AI systems. The presented system, ALPACA (Adaptive Learning Pipeline for Advanced Comprehensive AI Analysis), offers a comprehensive AI pipeline that addresses the needs of diverse user groups. ALPACA integrates visual and code-based development and facilitates all key phases of the AI pipeline. Its architecture is based on Celery (with Redis backend) for efficient task management, MongoDB for seamless data storage and Kubernetes for cloud-based scalability and resource utilisation. Future versions of ALPACA will support modern techniques such as federated and continuous learning as well as explainable AI methods to further improve security, usability and trustworthiness. The application is demonstrated by an Android app for similarity recognition, which emphasises ALPACA's potential for use in everyday life.
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