The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice

Companies struggle to continuously develop and deploy AI models to complex production systems due to AI characteristics while assuring quality. To ease the development process, continuous pipelines for AI have become an active research area where consolidated and in-depth analysis regarding the terminology, triggers, tasks, and challenges is required. This paper includes a Multivocal Literature Review where we consolidated 151 relevant formal and informal sources. In addition, nine-semi structured interviews with participants from academia and industry verified and extended the obtained information. Based on these sources, this paper provides and compares terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle management, and CD4ML. Furthermore, the paper provides an aggregated list of potential triggers for reiterating the pipeline, such as alert systems or schedules. In addition, this work uses a taxonomy creation strategy to present a consolidated pipeline comprising tasks regarding the continuous development of AI. This pipeline consists of four stages: Data Handling, Model Learning, Software Development and System Operations. Moreover, we map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.Comment: accepted in the Journal Systems and Softwar

MLOps: A Review

Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement

Design and Development of an MLOps Framework

Aquesta tesi explora el paper de MLOps per a proporcionar eficiència i productivitat en el desplegament, monitoratge i gestió de models d'aprenentatge automàtic en entorns de producció. L'informe compta amb una part teòrica que pretén aportar detalls sobre el funcionament de les pràctiques de MLOps i les tecnologies relacionades. Això serveix de complement per a la part pràctica, la qual es considera la principal contribució. Consisteix en el desenvolupament d'un framework que pretén recollir algunes de les principals funcionalitats de MLOps. Això ajuda a complir l'objectiu principal de demostrar la seva utilitat i comprendre per què MLOps és cada vegada més important. El desenvolupament del framework implica l'ús de Python, Docker, Streamlit i Airflow, cadascun necessari per a proporcionar diferents funcionalitats de MLOps