Analyzing Edge AI Deployment Challenges with in Hybrid IT Systems Utilizing Containerization and Blockchain-Based Data Provenance Solutions
Bibliographic record
Abstract
The integration of Edge AI within hybrid IT systems presents significant challenges, particularly in terms of scalability, security, and data integrity. This review explores the complexities of deploying Edge AI in hybrid environments, emphasizing the role of containerization and blockchain-based data provenance solutions in mitigating these challenges. Containerization enhances the portability and scalability of AI models across diverse edge devices and cloud infrastructures, while blockchain ensures secure and verifiable data lineage, addressing concerns related to data authenticity and regulatory compliance. The paper examines key deployment barriers, including resource constraints, interoperability issues, and latency considerations, alongside strategies for optimizing AI model efficiency in distributed computing environments. Additionally, it evaluates real world use cases, technological frameworks, and best practices for integrating containerized Edge AI solutions with blockchain-driven data provenance mechanisms. By bridging gaps in security, operational efficiency, and trust, this review highlights a pathway toward resilient and transparent Edge AI deployments within hybrid IT ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".