Ease. ML: A Lifecycle Management System for Machine Learning

Melgar, Leonel Aguilar; Dao, David; Gan, Shaoduo; Gürel, Nezihe Merve; Hollenstein, Nora; Jiang, Jiawei; Karlaš, Bojan; Lemmin, Thomas; Li, Tian; Yang, Li; Rao, Susie; Rausch, Johannes; Renggli, Cédric; Rimanic, Luka; Weber, Maurice; Zhang, Shuai; Zhao, Zhikuan; Schawinski, Kevin; Wu, Wentao; Zhang, Ce

doi:10.3929/ethz-b-000458916

Cited by 1 publication

(2 citation statements)

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Section: Discussionmentioning

confidence: 99%

“…More specifically, the first contribution of our work stems from bridging the gap between research on MLOps and IS literature. The emergence and rapid progress of MLOps have been increasingly noted by researchers and practitioners (Aguilar Melgar et al, 2021;Baier et al, 2019;Choudhary et al, 2022;Kolltveit and Li, 2022;Lwakatare et al, 2020a, b;Muralidhar et al, 2021;Paterson et al, 2021;Renggli et al, 2021). However, these efforts mainly led to a better understanding of the technical challenges pertaining to ML-based artifacts.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

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Riding a bicycle while building its wheels: the process of machine learning-based capability development and IT-business alignment practices

Mucha,

Ma,

Abhari

2023

INTR

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PurposeRecent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.Design/methodology/approachData were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.FindingsThe analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.Originality/valueThis study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

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Section: Discussionmentioning

confidence: 99%