| 1. |
- Gillblad, Daniel, 1975-
(författare)
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On practical machine learning and data analysis
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven meth- ods in e. g. industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelm- ing. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experi- enced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process.One of the most important issues when applying machine learning meth- ods to complex data, such as e. g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphi- cal models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available.Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for speci- fying the structure of e. g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent de- pendency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data.In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diag- nosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empiri- cal data, and show how the limited available background information can still be used initially for a functioning diagnosis system.To minimise the effort with which results are achieved within data anal- ysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment.Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include de- mand prediction for supply chain management, approximating complex simu- lators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system.
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| 2. |
- Isaksson, Martin, et al.
(författare)
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Adaptive Expert Models for Federated Learning
- 2023
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Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
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Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
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| 3. |
- John, Meenu Mary, et al.
(författare)
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Advancing MLOps from Ad hoc to Kaizen
- 2023
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Ingår i: Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023. ; , s. 94-101
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Konferensbidrag (refereegranskat)abstract
- Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.
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| 4. |
- John, Meenu Mary, et al.
(författare)
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Exploring Trade-Offs in MLOps Adoption
- 2023
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - 1530-1362. ; 30th Asia-Pacific Software Engineering Conference, APSEC 2023, s. 369-375
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Konferensbidrag (refereegranskat)abstract
- Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies. Based on our empirical results, we identify the key trade-offs we see companies make when adopting MLOps. We categorise these trade-offs into four concerns of the BAPO model: Business, Architecture, Process, and Organisation. Finally, we provide suggestions to mitigate the identified trade-offs. By identifying and detailing these trade-offs and the implications of these, this research helps companies to ensure the successful adoption of MLOps.
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