TY - GEN
T1 - Reference Architecture of MLOps Workflows
AU - Najafabadi, Faezeh Amou
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The rapid growth in the adoption of Machine Learning Operations Workflows (MLOps WFs) has given rise to the development of numerous guidelines and tools aimed at supporting the creation and management of these WFs. However, MLOps stakeholders continue to encounter challenges in employing these guidelines and tools. Firstly, there is a lack of consensus on the standard implementation of MLOps. Secondly, the current tools only support one or a number of components within MLOps workflows, complicating their integration into end-to-end WFs. Furthermore, the tendency towards automation in MLOps has led to uncertainty about the optimal level of human involvement, raising concerns about whether complete automation is the ideal approach. Responding to these issues, our goal in this research is to aid the MLOps WF stakeholders by providing a comprehensive reference architecture, that can be consulted as a basis of consolidated knowledge and experience in designing and managing MLOps WFs.
AB - The rapid growth in the adoption of Machine Learning Operations Workflows (MLOps WFs) has given rise to the development of numerous guidelines and tools aimed at supporting the creation and management of these WFs. However, MLOps stakeholders continue to encounter challenges in employing these guidelines and tools. Firstly, there is a lack of consensus on the standard implementation of MLOps. Secondly, the current tools only support one or a number of components within MLOps workflows, complicating their integration into end-to-end WFs. Furthermore, the tendency towards automation in MLOps has led to uncertainty about the optimal level of human involvement, raising concerns about whether complete automation is the ideal approach. Responding to these issues, our goal in this research is to aid the MLOps WF stakeholders by providing a comprehensive reference architecture, that can be consulted as a basis of consolidated knowledge and experience in designing and managing MLOps WFs.
KW - Machine Learning Operations
KW - MLOps
KW - MLOps Process
KW - MLOps Workflow
KW - Reference Architecture
UR - http://www.scopus.com/inward/record.url?scp=85204408601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85204408601&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71246-3_6
DO - 10.1007/978-3-031-71246-3_6
M3 - Conference contribution
AN - SCOPUS:85204408601
SN - 9783031709456
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 57
BT - Software Architecture. ECSA 2024 Tracks and Workshops
A2 - Ampatzoglou, Apostolos
A2 - Pérez, Jennifer
A2 - Buhnova, Barbora
A2 - Lenarduzzi, Valentina
A2 - Venters, Colin C.
A2 - Zdun, Uwe
A2 - Drira, Khalil
A2 - Rebelo, Luciana
A2 - Di Pompeo, Daniele
A2 - Tucci, Michele
A2 - Nakagawa, Elisa Yumi
A2 - Navarro, Elena
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th European Conference on Software Architecture, ECSA 2024
Y2 - 3 September 2024 through 6 September 2024
ER -