Design Methods and Processes for ML/DL models

Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are complex. Regardless of the level of expertise and/or access to data scientists, companies in all embedded systems domain struggle to build high-performing models due to a lack of established and systematic design methods and processes. Objective: The overall objective is to establish systematic and structured design methods and processes for the end-to-end process of developing, deploying and successfully evolving ML/DL models. Method: To achieve the objective, we conducted our research in close collaboration with companies in the embedded systems domain using different empirical research methods such as case study, action research and literature review. Results and Conclusions: This research provides six main results: First, it identifies the activities that companies undertake in parallel to develop, deploy and evolve ML/DL models, and the challenges associated with them. Second, it presents a conceptual framework for the continuous delivery of ML/DL models to accelerate AI-driven business in companies. Third, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on how to integrate, deploy and operationalize ML/DL models. Fourth, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge. These architectural alternatives range from a centralized architecture that prioritizes (re)training in the cloud to a decentralized architecture that prioritizes (re)training at the edge. Fifth, it identifies key factors to help companies decide which architecture to choose for deploying ML/DL models. Finally, it explores how MLOps, as a practice that brings together data scientist teams and operations, ensures the continuous delivery and evolution of models.

A Schema for Systematic Service Imagining: Context-Based Activity Modeling

Design thinking as a mindset and as a process for design and business innovation receives a lot of attention. Thus, concrete and structured methods for design thinking need to be devised, and design thinking competencies should be fostered proactively. Design thinking is underpinned by visual thinking composed of interactive iterations of Seeing—Imagining—Drawing. The visual reasoning model developed to understand and support visual thinking describes the process with cognitive activities as well as knowledge and schema. The visual reasoning model could serve as a framework to devise structured methods and tools for design thinking and to foster design thinking competencies. It would be desirable if schema to serve as underlying models in imagining service activities are to be identified so that cognitive activities in seeing—imagining—drawing can be systematically structured in service design, where the objects of designing are human activities and experiences. In this paper, three structured design methods developed for service design have been described and characterized in the framework of the visual reasoning model. Particularly the context-based activity modeling has been demonstrated as schema in structured imagining of service activities for product-service systems, as it serves the underlying role in organizing information on human activities consistently and yet with different interactions with other constituents of these three imagining methods.

Multiprocessor system for the real-time digital processing of video-image series / Ein Multiprozessorsystem für die digitale Verarbeitung von Fernsehbildserien in Echtzeit

In the course of a project for the analysis of X-ray picture series a new system for the digital processing of video-image series has been developed. It allows the real time digitization (up to 512 pixels/line, 8 bits/pixel), and high speed processing of video-picture series and a presentation of results in an easily interpretable way by a colour TV-display processor. As a characteristic of the system the extreme requirements concerning the digitization and processing speed (with data rates up to 80 Mbaud) are met by a realization in the form of a multiprocessor structure. The microprogrammed special processors communicate via a high speed asynchronous bus with a throughput of 15 Mwords/ s. Their functions are controlled by the commands of a host computer (e.g. PDP U/45). In order to achieve the above performance, structured design methods such as Petri-nets have been applied, which give rise to a more transparent design, better documentation and shorter implementation times. This paper describes the design, the structure, the programming and the application of the system.