… Which challenges in the application and deployment of machine learning can we identify in
… organized along the categories pre-deployment, deployment and non-technical challenges. …

… and deploy two di erent machine learning pipelines. • Proactive training of the deployed models
… scenario from a smaller number of disk I/O operations. To analyze the e ect of the online …

… machine learning deployment workflow, we show that practitioners face issues at each stage
of the deployment … Operational support Reuse of code and models Software engineering …

… to revolutionize the conventional operation and structure of … -driven operations to proactive/data-driven
operations. This … intelligence as well as machine-learning-based air interface …

… on either VNF deployment or SFC routing path decisions, or both at the same. In this paper,
we propose a machine learning model to decide the optimal VNF deployment in the Multi-…

… This article introduces the importance of machine learning in real-world applications … Machine
Learning Operations) and its importance for solving challenges such as model deployment …

… Hence, efficient management and operation of such networks requires a high degree of …
To enable fast and proactive decisions in this context, we propose a machine learning-based …

… on the machine learning lifecycle and … of machine learning frameworks in the MLOps
approach. Subsequently, we deep dive into push and pull-based deployments in Github Operations …

… ArcGIS and machine learning to optimize the deployment and management of BSSs for E2Ws.
… machine learning model proposed in this study provides a solution for operators to deploy …

Deploying machine learning models to production is challenging, partially due to the misalignment
between software engineering and machine learning … practices in machine learning, …