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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.
In modern industrial production lines, the integration and interconnection of various different manufacturing components, like robots, laser cutting machines, milling machines, CNC-machines, etc. allows for a higher degree of autonomous production on the shop floor. Manufacturers of these increasingly complex machines are beginning to equip their business models with bidirectional data flows to other factories. This is creating a digital, cross-company shop floor infrastructure where the transfer of information is controlled by digital contracts. To establish a trusted ecosystem, the new technology "blockchain" and a variety of technology stacks must be combined while ensuring security. Such blockchain-based frameworks enable bidirectional trust across all contract partners. Essential data flows are defined by specific technical representation of contract agreements and executed through smart contracts.This work describes a platform for rapid cross-company business model instantiation based on blockchain for establishing trust between the enterprises. It focuses on selected security aspects of the deployment- and configuration processes applied by the industrial ecosystem. A threat analysis of the platform shows the critical security risks. Based on an industrial dynamic machine leasing use case, a risk assessment and security analysis of the key platform components is carried out.