Operating Enterprise AI as a Service

Casati, Fabio; Govindarajan, K.; Jayaraman, Baskar; Thakur, Aniruddha; Palapudi, Sriram; Karakusoglu, Firat; Chatterjee, Debu

doi:10.1007/978-3-030-33702-5_25

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Drawing on the technical heritage of Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service, many organisations are adopting AIaaS, accessing specific AI capabilities through the cloud (Casati et al., 2019; Parsaeefard et al., 2019; Tubaro et al., 2020). In the same way that cloud technologies have democratised access to software, data storage and data processing (Mosco, 2015), AIaaS empowers organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms.…”

Section: Ai-as-a-servicementioning

confidence: 99%

“…AIaaS, particularly machine-learning based AIaaS, is data intensive and thus labour intensive (Casati et al., 2019; Tubaro and Casilli, 2019). Machine-learning models must be trained with accurate, relevant, and high-quality training datasets (von Krogh, 2018).…”

Section: The Invisible Backstage Of Ai Productionmentioning

confidence: 99%

“…In the current zeitgeist of research into artificial intelligence (AI), academic interest has largely focused on AI as an abstract sociotechnical phenomenon, or as embedded in major platforms such as Facebook, Google or Twitter. However, this focus has overlooked the widespread growth of commercial AI-as-a-Service offerings (AIaaS), where organisations access specific AI capabilities via cloud computing (Casati et al., 2019; Parsaeefard et al., 2019; Tubaro et al., 2020). Enabling organisations to scale their AI usage based on strategic requirements, AIaaS can range from conversational bots to knowledge mapping, computer vision and speech recognition.…”

Section: Introductionmentioning

confidence: 99%

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Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

Newlands

2021

Big Data & Society

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Artificial Intelligence-as-a-Service (AIaaS) empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations.

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Section: Ai-as-a-servicementioning

confidence: 99%

Section: The Invisible Backstage Of Ai Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

Newlands

2021

Big Data & Society

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“…Artificial Intelligence-as-a-Service (AIaaS) has also been considered in [ 41 , 42 , 43 ] as a natural extension of the usual “as-a-service” (“aaS”) service delivery models available with cloud providers. This allows developers to focus on their domain-specific details and conveniently add AI capability to their software.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Several works have shown the benefits of AIaaS in developing and supporting applications that require AI capabilities. Casati et al [ 42 ] proposed a framework and architecture that facilitate the deployment of cloud-based AIaaS for smarter enterprise management solutions. Milton et al [ 43 ] conducted real experiments utilizing Google’s Dialogflow API (a simple AIaaS provided by Google) [ 44 ] to develop a chatbot.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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