Machine learning operations (mlops): Overview, definition, and architecture
D Kreuzberger, N Kühl, S Hirschl - IEEE access, 2023 - ieeexplore.ieee.org
The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …
rapidly bring them into production. However, it is highly challenging to automate and …
Operationalizing machine learning models: A systematic literature review
AB Kolltveit, J Li - Proceedings of the 1st Workshop on Software …, 2022 - dl.acm.org
Deploying machine learning (ML) models to production with the same level of rigor and
automation as traditional software systems has shown itself to be a non-trivial task, requiring …
automation as traditional software systems has shown itself to be a non-trivial task, requiring …
MLOps: a taxonomy and a methodology
Over the past few decades, the substantial growth in enterprise-data availability and the
advancements in Artificial Intelligence (AI) have allowed companies to solve real-world …
advancements in Artificial Intelligence (AI) have allowed companies to solve real-world …
[HTML][HTML] The pipeline for the continuous development of artificial intelligence models—Current state of research and practice
Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …
complex production systems due to AI characteristics while assuring quality. To ease the …
AI lifecycle models need to be revised: An exploratory study in Fintech
Tech-leading organizations are embracing the forthcoming artificial intelligence revolution.
Intelligent systems are replacing and cooperating with traditional software components …
Intelligent systems are replacing and cooperating with traditional software components …
[PDF][PDF] MLOps: Practices, Maturity Models, Roles, Tools, and Challenges-A Systematic Literature Review.
A Lima, L Monteiro, AP Furtado - ICEIS (1), 2022 - scitepress.org
Context: The development of machine learning solutions has increased significantly due to
the advancement of technology based on artificial intelligence. MLOps have emerged as an …
the advancement of technology based on artificial intelligence. MLOps have emerged as an …
Advancing MLOps from Ad hoc to Kaizen
MM John, D Gillblad, HH Olsson… - 2023 49th Euromicro …, 2023 - ieeexplore.ieee.org
Companies across various domains increasingly adopt Machine Learning Operations
(MLOps) as they recognise the significance of operationalising ML models. Despite growing …
(MLOps) as they recognise the significance of operationalising ML models. Despite growing …
Empowering smart cities with digital twins of buildings: Applications and implementation considerations of data-driven energy modelling in building management
Smart buildings and cities are rapidly emerging as solutions to address the challenges of
efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data …
efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data …
Function+ Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
The development of digital twins (DTs) for physical systems increasingly leverages artificial
intelligence (AI), particularly for combining data from different sources or for creating …
intelligence (AI), particularly for combining data from different sources or for creating …
The Past, Present, and Future of Research on the Continuous Development of AI
Since 2020, 33 literature reviews have systematically synthesized research on the
continuous development of AI, also known as Machine Learning Operations (MLOps) …
continuous development of AI, also known as Machine Learning Operations (MLOps) …