Toward Building Edge Learning Pipelines

Gounaris, Anastasios; Michailidou, Anna-Valentini; Dustdar, Schahram

doi:10.1109/mic.2022.3171643

Cited by 2 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, a lot of efforts have been recently devoted to increasing data quality with the help of automated pipelines, data engineering frameworks, and prototypes. The implementation of technical solutions like data lakes [105], low-latency data infrastructure [106], feature stores [107], data warehouses [108,109], data branching [110], AutoML for data management [111], data strew-ships [112], data fusion techniques [113], data taxonomies [114], data-quality enhancement pipelines [115], data mesh and fabric [116], addressing imbalances in data [117], smart bots for data quality enhancement [118], data ontologies [119], data quality evaluation metrics [120], synthetic data generation tools [120], data profiling [121], reference stores for data quality [122], and data validation pipelines [123,124], to name a few, are vastly contributing in the feasibility and affordability of DC-AI-based solutions. In the future, more developments are expected in data quality enhancement, leading to the realization of DC-AI across many enterprises.…”

Section: Analysis Of the Feasibility And Affordability Of Dc-ai-based...mentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

View full text Add to dashboard Cite

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

show abstract