Jove
Visualize
联系我们

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
34
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

60
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
60

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same journal

Plastic particles in three Brazilian Federal Conservation Units: are aquatic matrices really protected?

Anais da Academia Brasileira de Ciencias·2026
Same journal

The Brazilian Reproducibility Network.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Effect of indigo on the reproductive biology of Aedes aegypti.

Anais da Academia Brasileira de Ciencias·2026
Same journal

The Lack of Reviewers Pandemic (LRP) - what can be done?

Anais da Academia Brasileira de Ciencias·2026
Same journal

Optimization of Horizontal-Axis Turbine Blades under Drivetrain Resistance.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Tracing the Maternal Lineages and Historical Biogeography of southern South American Hollies (Ilex, Aquifoliaceae).

Anais da Academia Brasileira de Ciencias·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: May 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

466

为机器学习操作制定或购买策略 - MLOps

Diego Nogare1,2, Ismar F Silveira1, Renato Banzai2

  • 1Universidade Presbiteriana Mackenzie, Programa de Pós-Graduação em Engenharia Elétrica e Computação - PPGEEC, Rua da Consolação, 930, 01302-907 São Paulo, SP, Brazil.

Anais da Academia Brasileira de Ciencias
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

组织在决定是否构建或购买机器学习操作 (MLOps) 解决方案时,必须权衡成本,专业知识和战略一致性. 这项研究指导了MLOps的战略,分析了有效的机器学习模型生命周期管理的工具.

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

相关实验视频

Last Updated: May 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

466
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 商业战略 商业战略

背景情况:

  • 组织面临着关于机器学习操作 (MLOps) 实施的复杂决策.
  • 对于MLOps的"制造或购买"战略,需要平衡成本,质量,专业知识和战略目标.

研究的目的:

  • 分析MLOps解决方案的制造或购买策略.
  • 为组织提供指南,帮助组织在MLOps能力的内部开发和外部采购之间做出决定.
  • 评估各种MLOps工具用于机器学习模型生命周期管理.

主要方法:

  • 对MLOps工具进行定性和定量审查.
  • 分析包括成本,质量,技术专业知识和战略调整在内的因素.
  • 在MLOps中探索产品复杂性,核心能力和风险管理.

主要成果:

  • 介绍了一个评估MLOps"做或买"决策的框架.
  • 提供了对流行的MLOps平台如MLFlow,Airflow,Kubeflow,Databricks,Dataiku,H2O,AWS,Azure和GCP进行比较的评论.
  • 在工具选择中强调了项目特定需求的重要性.

结论:

  • 有效的MLOps实施取决于明确的战略和适当的工具选择.
  • 了解组织能力和项目要求对于成功采用MLOps至关重要.
  • 这项研究提供了关于如何在MLOps环境中获得竞争优势的见解.