An Analysis of MLOps Architectures: A Systematic Mapping Study

• URL: http://arxiv.org/abs/2406.19847v1
• Date: Fri, 28 Jun 2024 11:38:50 GMT
• Title: An Analysis of MLOps Architectures: A Systematic Mapping Study
• Authors: Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos, Patricia Lago,
• Abstract summary: This study provides an overview of the state of the art in MLOps from an architectural perspective. Researchers and practitioners can use our findings to inform the architecture design of their MLOps systems.
• Score: 12.399094410444743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for MLOps, there is simultaneously a lack of consolidated architecture knowledge that can inform the architecture design. Objective. Our primary objective is to provide a comprehensive overview of (i) how MLOps architectures are defined across the literature and (ii) which tools are mentioned to support the implementation of each architecture component. Method. We apply the Systematic Mapping Study method and select 43 primary studies via automatic, manual, and snowballing-based search and selection procedures. Subsequently, we use card sorting to synthesize the results. Results. We contribute (i) a categorization of 35 MLOps architecture components, (ii) a description of several MLOps architecture variants, and (iii) a systematic map between the identified components and the existing MLOps tools. Conclusion. This study provides an overview of the state of the art in MLOps from an architectural perspective. Researchers and practitioners can use our findings to inform the architecture design of their MLOps systems.

Related papers

• A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis [8.629569588488328]
  This paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focus on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area.
  arXiv  Detail & Related papers  (2024-10-22T19:12:03Z)
• EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition [54.99121380536659]
  Eye movement biometrics have received increasing attention thanks to its high secure identification. Deep learning (DL) models have been recently successfully applied for eye movement recognition. DL architecture still is determined by human prior knowledge. We propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition.
  arXiv  Detail & Related papers  (2024-09-22T13:11:08Z)
• Masked Image Modeling: A Survey [73.21154550957898]
  Masked image modeling emerged as a powerful self-supervised learning technique in computer vision. We construct a taxonomy and review the most prominent papers in recent years. We aggregate the performance results of various masked image modeling methods on the most popular datasets.
  arXiv  Detail & Related papers  (2024-08-13T07:27:02Z)
• Comparison of Static Analysis Architecture Recovery Tools for Microservice Applications [43.358953895199264]
  We will identify static analysis architecture recovery tools for microservice applications via a multi-vocal literature review. We will then execute them on a common dataset and compare the measured effectiveness in architecture recovery.
  arXiv  Detail & Related papers  (2024-03-11T17:26:51Z)
• Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision [51.88848982611515]
  Unsupervised graph neural architecture search remains unexplored in the literature. We propose a novel Disentangled Self-supervised Graph Neural Architecture Search model. Our model is able to achieve state-of-the-art performance against several baseline methods in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-03-08T05:23:55Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications. We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
  The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
  arXiv  Detail & Related papers  (2023-08-09T21:54:34Z)
• A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems [16.690434072032176]
  We show how compositional thinking can provide rules for the creation and management of architectural frameworks for complex systems. The aim of the paper is not to provide viewpoints or architecture models specific to AI systems, but instead to provide guidelines on how a consistent framework can be built up with existing, or newly created, viewpoints.
  arXiv  Detail & Related papers  (2022-12-27T18:05:02Z)
• Mining Architectural Information: A Systematic Mapping Study [7.3755596064775215]
  There is a lack of clarity on what literature on mining architectural information is available. We aim to identify, analyze, and synthesize the literature on mining architectural information.
  arXiv  Detail & Related papers  (2022-12-26T14:57:38Z)
• Multi-Perspective LSTM for Joint Visual Representation Learning [81.21490913108835]
  We present a novel LSTM cell architecture capable of learning both intra- and inter-perspective relationships available in visual sequences captured from multiple perspectives. Our architecture adopts a novel recurrent joint learning strategy that uses additional gates and memories at the cell level. We show that by using the proposed cell to create a network, more effective and richer visual representations are learned for recognition tasks.
  arXiv  Detail & Related papers  (2021-05-06T16:44:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.