Management of Machine Learning Lifecycle Artifacts: A Survey

• URL: http://arxiv.org/abs/2210.11831v1
• Date: Fri, 21 Oct 2022 09:23:12 GMT
• Title: Management of Machine Learning Lifecycle Artifacts: A Survey
• Authors: Marius Schlegel, Kai-Uwe Sattler
• Abstract summary: We aim to give an overview of systems and platforms which support the management of machine learning lifecycle artifacts. Based on a systematic review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.
• Score: 7.106986689736826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.

Related papers

• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• Machine Learning Operations: A Mapping Study [0.0]
  This article discusses the issues that exist in several components of the MLOps pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study.
  arXiv  Detail & Related papers  (2024-09-28T17:17:40Z)
• The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
  Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
  arXiv  Detail & Related papers  (2024-06-24T15:55:49Z)
• Data Collection and Labeling Techniques for Machine Learning [0.0]
  Data collection and labeling are critical bottlenecks in the deployment of machine learning applications. This paper provides a review of the state-of-the-art methods in data collection, data labeling, and the improvement of existing data and models.
  arXiv  Detail & Related papers  (2024-06-19T06:01:28Z)
• 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)
• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models [33.50873478562128]
  Large Language Models (LLMs) bring forth challenges in the high consumption of computational, memory, energy, and financial resources. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs.
  arXiv  Detail & Related papers  (2024-01-01T01:12:42Z)
• Model-driven Engineering for Machine Learning Components: A Systematic Literature Review [8.810090413018798]
  We analyzed studies with respect to several areas of interest and identified the key motivations behind using MDE4ML. We also discuss the gaps in existing literature and provide recommendations for future work.
  arXiv  Detail & Related papers  (2023-11-01T04:29:47Z)
• Retrieval-Enhanced Machine Learning [110.5237983180089]
  We describe a generic retrieval-enhanced machine learning framework, which includes a number of existing models as special cases. REML challenges information retrieval conventions, presenting opportunities for novel advances in core areas, including optimization. REML research agenda lays a foundation for a new style of information access research and paves a path towards advancing machine learning and artificial intelligence.
  arXiv  Detail & Related papers  (2022-05-02T21:42:45Z)
• Lifelong Learning Metrics [63.8376359764052]
  The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems. This document outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios.
  arXiv  Detail & Related papers  (2022-01-20T16:29:14Z)
• 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)
• An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration [55.67505546330206]
  We give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques. In this paper, we give examples of the application of recommender systems and machine learning and discuss future research issues.
  arXiv  Detail & Related papers  (2021-02-12T17:21:36Z)

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.