mlsanityData to 14 Oct 2026

ALPACA -- Adaptive Learning Pipeline for Comprehensive AI

arXiv 2412.10950 ↗First posted 14 Dec 2024cs.DC cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 34 references that are in the map. First posted on arXiv 14 Dec 2024.

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2 of 34 references.

References · 2

  1. 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
  2. 2022Review of the state of the art in autonomous artificial intelligencecited by 3

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