Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning

• URL: http://arxiv.org/abs/2405.09819v1
• Date: Thu, 16 May 2024 05:36:28 GMT
• Title: Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning
• Authors: Penghao Liang, Bo Song, Xiaoan Zhan, Zhou Chen, Jiaqiang Yuan,
• Abstract summary: Article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity.
• Score: 5.565764053895849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.

Related papers

• Process Modeling With Large Language Models [42.0652924091318]
  This paper explores the integration of Large Language Models (LLMs) into process modeling. We propose a framework that leverages LLMs for the automated generation and iterative refinement of process models. Preliminary results demonstrate the framework's ability to streamline process modeling tasks.
  arXiv  Detail & Related papers  (2024-03-12T11:27:47Z)
• Towards MLOps: A DevOps Tools Recommender System for Machine Learning System [1.065497990128313]
  MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. In this paper, we present a framework for recommendation system that processes the contextual information. Four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated.
  arXiv  Detail & Related papers  (2024-02-20T09:57:49Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Exploring MLOps Dynamics: An Experimental Analysis in a Real-World Machine Learning Project [0.0]
  The experiment involves a comprehensive MLOps workflow, covering essential phases like problem definition, data acquisition, data preparation, model development, model deployment, monitoring, management, scalability, and governance and compliance. A systematic tracking approach was employed to document revisits to specific phases from a main phase under focus, capturing the reasons for such revisits. The resulting data provides visual representations of the MLOps process's interdependencies and iterative characteristics within the experimental framework.
  arXiv  Detail & Related papers  (2023-07-22T10:33:19Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)
• Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
  democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
  arXiv  Detail & Related papers  (2020-07-07T08:34:48Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

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.