TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Ruf, Philipp
A1  - Madan, Manav
A1  - Reich, Christoph
A1  - Ould-Abdeslam, Djaffar
T1  - Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
JF  - Applied Sciences
N2  - Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.
KW  - MLOps Mlflow DVC
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-77934
SN  - 2076-3417
SS  - 2076-3417
U6  - https://doi.org/10.3390/app11198861
DO  - https://doi.org/10.3390/app11198861
VL  - 11.2021
IS  - 19
SP  - 39
S1  - 39
ER  -