Browsing by Subject "Machine Learning"

The rapid growth of artificial intelligence (AI) and machine learning (ML) solutions has created a need to develop, deploy and maintain AI/ML those to production reliably and efficiently. MLOps (Machine Learning Operations) framework is a collection of tools and practices that aims to address this challenge. Within the MLOps framework, a concept called the feature store is introduced, serving as a central repository responsible for storing, managing, and facilitating the sharing and reuse of extracted features derived from raw data. This study gives first an overview of the MLOps framework and delves deeper into feature engineering and feature data management, and explores the challenges related to these processes. Further, feature stores are presented, what they exactly are and what benefits do they introduce to organizations and companies developing ML solutions. The study also reviews some of the currently popular feature store tools. The primary goal of this study is to provide recommendations for organizations to leverage feature stores as a solution to the challenges they encounter in managing feature data currently. Through an analysis of the current state-of-the-art and a comprehensive study of organizations' practices and challenges, this research presents key insights into the benefits of feature stores in the context of MLOps. Overall, the thesis highlights the potential of feature stores as a valuable tool for organizations seeking to optimize their ML practices and achieve a competitive advantage in today's data-driven landscape. The research aims to explore and gather practitioners' experiences and opinions on the aforementioned topics through interviews conducted with experts from Finnish organizations.

Modularity is often used to manage the complexity of monolithic software systems. This is done through reducing maintenance costs by minimizing the entanglement in software code and functionality. Modularity also lowers future development costs through enabling the reuse and stacking of different types of modular functionality and software code for different environments and software engineering problems. Although there are important differences between the problem solving processes and practices of machine learning system developers and software engineering developers, machine learning system developers have been shown to be able to adopt a lot from traditional software engineering. A systematic literature review is used to identify 484 studies published in four electronic sources from January 1990 to October 2021. After examination of papers, statistical and qualitative results are formed for selected 86 studies which provide sufficient information regarding the presence of modular operators and comparison to monolithic solutions. The selected studies addressed a wide number of different tasks and domains, which saw performance benefits compared to monolithic machine learning and deep learning methods. Nearly two thirds of studies discovered Modular Neural Networks (MNNs) providing improvements in task accuracy when compared to monolithic solutions. Only 16,3\% of studies reported efficiency values in their comparisons. Over 82,5\% of studies that reported their MNNs efficiency found benefits in computation time, memory/size and energy consumption when compared to monolithic solutions. The majority of studies were carried out in laboratory environments on singular focused tasks and static requirements, which may have limited the visibility of modular operators. MNNs show positive promise for performance and efficiency in machine learning. More comparable studies are needed, especially from the industry, that use MMNs in constantly changing requirements and thus apply multiple modular operators.

Ubiquitous sensing is transforming our societies and how we interact with our surrounding envi- ronment; sensors provide large streams of data while machine learning techniques and artificial intelligence provide the tools needed to generate insights from the data. These developments have taken place in almost every industry sector with topics such as smart cities and smart buildings becoming key topical issues as societies seek more sustainable ways of living. Smart buildings are the main context of this thesis. These are buildings equipped with various sensors used to collect data from the surrounding environment allowing the building to adapt itself and increasing its operational efficiency. Previously, most efforts in realizing smart buildings have focused on energy management and au- tomation where the goal is to improve costs associated with heating, ventilation, and air condi- tioning. A less studied area involves smart buildings and their indoor environments especially relative to sub-spaces within a building. Increased developments in low-cost sensor technologies have created new opportunities to sense indoor environments in more granular ways that provide new possibilities to model finer attributes of spaces within a building. This thesis focuses on modeling indoor environment data obtained from a multipurpose building that serves primarily as a school. The aim is to explore the quality of the indoor environment relative to regulatory guidelines and also exploring suitable predictive models for thermal comfort and indoor air quality. Additionally, design science methodology is applied in the creation of a proof of concept software system. This system is aimed at demonstrating the use of Web APIs to provide sensor data to clients that may use the data to render analytics among other insights to a building’s stakeholders. Overall, the main technical contributions of this thesis are twofold: (i) a potential web-application design for indoor air quality IoT data and (ii) an exposition of modeling of indoor air quality data based on a variety of sensors and multiple spaces within the same building. Results indicate a software-based tool that supports monitoring the indoor environment of a building would be beneficial in maintaining the correct levels of various indoor parameters. Further, modeling data from different spaces within the building shows a need for heterogeneous models to predict variables in these spaces. This implies parameters used to predict thermal comfort and air quality are different in varying spaces especially where the spaces differ in size, indoor climate control settings, and other attributes such as occupancy control.