TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Caycioglu, Malik
A1  - Schlegel, Dennis
T1  - A criteria framework for the evaluation of cloud-based machine learning services
JF  - International journal of management cases
N2  - Artificial intelligence (AI) is one of the most promising technologies of the post-pandemic era. Cloud computing technology can simplify the process of developing AI applications by offering a variety of services, including ready-to-use tools to train machine learning (ML) algorithms. However, comparing the vast amount of services offered by different providers and selecting a suitable cloud service can be a major challenge for many firms. Also in academia, suitable criteria to evaluate this type of service remain largely unclear. Therefore, the overall aim of this work has been to develop a framework to evaluate cloud-based ML services. We use Design Science Research as our methodology and conduct a hermeneutic literature review, a vendor analysis, as well as, expert interviews. Based on our research, we present a novel framework for the evaluation of cloud-based ML services consisting of six categories and 22 criteria that are operationalized with the help of various metrics. We believe that our results will help organizations by providing specific guidance on how to compare and select service providers from the vast amount of potential suppliers.
KW  - machine learning as a service
KW  - MLaaS
KW  - Artificial Intelligence as a service
KW  - AIaaS
KW  - cloud AI
Y1  - 2023
UR  - https://www.circleinternational.co.uk/journals/ijmc/current-past-issues/
SN  - 1741-6264
SS  - 1741-6264
VL  - 25
IS  - 1
SP  - 31
EP  - 48
S1  - 18
PB  - Pallas Press
CY  - Cheltenham
ER  -