Foundational Framework Self-Healing Data Pipelines for AI Engineering: A Framework and Implementation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P107Keywords:
Self-Healing Data Pipelines, AI Engineering, Data Quality Management, Fault-Tolerant Systems, Automated Data Recovery, Real-Time Data Processing, Data Pipeline Framework, Resilient Data Architectures, Continuous Data Validation, Error Detection and Correction, Adaptive Data Systems, Data Reliability, End-to-End Data Pipelines, AI Workflow AutomationAbstract
The increasing complexity of AI engineering workflows necessitates data pipelines that can autonomously detect, isolate, and recover from faults without human intervention. This paper presents a formal architectural framework for self-healing data pipelines tailored for AI engineering environments. The proposed model defines the core functional modules, their interdependencies, and the control mechanisms required to sustain continuous, reliable data flow in dynamic, high-throughput contexts. The framework operates on the premise that fault tolerance should not be an afterthought but a foundational design principle, enabling systems to adapt to data anomalies, infrastructure failures, and model degradation in real time. We develop a generalized reference architecture that is domain-agnostic, allowing for integration across varied AI engineering applications. The methodology emphasizes theoretical constructs over direct implementation, employing architecture diagrams, formal interaction protocols, and conceptual workflow sequences to model detection, diagnosis, and remediation processes. This theoretical focus enables the framework to serve as a baseline for diverse future implementations without being constrained by specific technologies or platforms. Conceptually, the findings establish that modular, self-aware pipeline architecture anchored in principles of redundancy, autonomous decision-making, and continuous monitoring can provide the structural resilience necessary for mission-critical AI engineering operations. By delineating fault-handling logic, state transition rules, and system recovery pathways, the model offers a repeatable blueprint that subsequent phases of research and development can extend into operational systems. This foundational framework serves as the intellectual cornerstone upon which adaptive, scalable, and explainable self-healing data pipeline solutions can be built, ensuring the robustness and trustworthiness of AI engineering ecosystems. Self-healing data pipelines, AI engineering, fault tolerance, autonomous recovery, pipeline architecture, fault detection, theoretical framework, resilience, high-throughput systems, system reliability, adaptive architecture, domain-agnostic design, continuous monitoring, redundancy, conceptual workflow
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