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.
Towards Regulatory-Compliant MLOps: Orazivio’s Journey from a Machine Learning Experiment to a Deployed Certified Medical Product
