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市场调查报告书
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1738534

全球人工智慧和机器学习营运软体市场：按应用、部署、功能、最终用户和地区分類的范围和预测

Global AI & Machine Learning Operationalization Software Market By Application, By Deployment, By Functionality, By End-User, & By Geographic Scope And Forecast
出版日期: | 出版商: Verified Market Research | 英文 202 Pages | 商品交期: 2-3个工作天内

价格
简介目录

人工智慧/机器学习操作化软体市场规模与预测

人工智慧和机器学习操作化软体市场规模预计在 2024 年达到 61.2 亿美元，预计在 2026 年至 2032 年期间的复合年增长率为 35.2%，到 2032 年将达到 362.5 亿美元。

人工智慧和机器学习操作化软体（MLOps 软体）正在简化机器学习模型的生命週期，将其从开发转移到实际应用。

透过自动执行模型部署、监控和管治等任务，MLOps 软体可确保这些模型有效可靠地运作。

这意味着效率提高、成本降低和创新週期加快等好处。

MLOps 软体最终透过利用人工智慧和机器学习的力量来完成诈欺侦测、个人化建议和预测性维护等任务，从而实现巨大的商业价值。

全球人工智慧和机器学习操作化软体市场动态

影响人工智慧和机器学习操作化软体市场的关键市场动态是：

关键市场驱动因素

人工智慧 (AI) 和机器学习 (ML) 应用激增：人工智慧 (AI) 和机器学习 (ML) 在各行各业的广泛应用，主要源自于需求激增。越来越多的企业利用人工智慧和机器学习来实现自动化、决策和流程最佳化等任务，这推动了对 MLOps 软体的需求，以便有效地管理和操作这些模型。

需要简化的工作流程：开发、部署和管理机器学习模型的复杂性需要简化的工作流程。 MLOps 软体可以满足这项需求，它可以自动执行模型部署、监控和管治等任务。这种自动化可以提高效率、减少错误，并加快 AI倡议的价值实现时间。

更重视模型管治和可解释性：围绕人工智慧和机器学习应用的监管审查日益严格，导致人们更加关注模型管治和可解释性。 MLOps 软体透过提供模型管治和可解释性等功能，在这方面发挥关键作用。这些功能可确保合规性，并提高已部署模型的透明度，从而增强信任并促进更广泛的采用。

云端采用和可扩展性：云端运算的快速普及为 MLOps 软体供应商带来了机会。云端基础方案提供的可扩展性和成本效益使其成为各种规模组织的理想选择。 MLOps 软体市场的成长正源自于这种向云端环境的转变。

主要问题

整合复杂性：将 MLOps 软体与现有企业系统整合可能是一项复杂的任务。资料孤岛、不同的技术堆迭以及缺乏标准化会在实施过程中造成障碍，妨碍顺利运作。

可解释性和可信度：随着围绕人工智慧的监管和伦理考虑日益凸显，确保机器学习模型的可解释性和可信度至关重要。 MLOps 软体必须能够展示模型如何得出决策，从而提升信任度并促进合规性。

技能人才短缺：人工智慧和机器学习的快速发展，对精通 MLOps 工具和技术的技能型专业人员的需求庞大。这种人才短缺可能会限制组织有效部署和管理其 MLOps 基础设施的能力。

主要趋势

自动化浪潮：市场见证了 MLOps 软体自动化功能的不断提升。这包括模型部署、监控和管理等任务的自动化。这意味着 AI 解决方案的效率提升、成本降低和上市缩短时间。

专注于安全性和可解释性：有关人工智慧和机器学习使用的法规日益增多，推动了对 MLOps 软体中模型管治和可解释性等功能的重视，这些功能可确保在实际应用中部署的人工智慧模型的合规性、透明度和负责任的使用。

开放原始码选项的兴起：蓬勃发展的开放原始码MLOps 社群为企业提供了经济高效的选择，促进了创新并扩大了 MLOps 工具的可及性。然而，现有供应商预计将凭藉全面的解决方案和可靠的支援服务，继续保持相当大的市场占有率。

目录

第一章 全球人工智慧与机器学习操作化软体市场简介

  • 市场概览
  • 研究范围
  • 先决条件

第二章执行摘要

第三章：已验证的市场研究调查方法

  • 资料探勘
  • 验证
  • 第一手资料
  • 资料来源列表

第四章 人工智慧与机器学习操作化软体的全球市场展望

  • 概述
  • 市场动态
    • 驱动程式
    • 限制因素
    • 机会
  • 波特五力模型
  • 价值链分析

第五章 全球人工智慧和机器学习操作化软体市场（按应用）

  • 预测分析
  • 自然语言处理
  • 电脑视觉
  • 语音辨识
  • 异常检测

第六章 全球人工智慧和机器学习操作化软体市场（按部署）

  • 本地
  • 云端基础
  • 杂交种

第七章全球人工智慧和机器学习操作化软体市场（按功能）

  • 模型部署和管理
  • 资料预处理和特征工程
  • 模型监控与效能评估
  • 与现有系统集成

第八章。全球人工智慧和机器学习操作化软体市场（按最终用户）

  • 卫生保健
  • 金融
  • 零售
  • 製造业
  • 车
  • 政府
  • 媒体娱乐
  • 通讯
  • 能源公共产业
  • 教育

第九章全球人工智慧和机器学习操作化软体市场（按地区）

  • 概述
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 其他亚太地区
  • 其他的
    • 拉丁美洲
    • 中东和非洲

第十章全球人工智慧与机器学习操作化软体市场的竞争格局

  • 概述
  • 各公司市场排名
  • 重点发展策略

第十一章 公司简介

  • Algorithmia
  • Logical Clocks
  • Spell
  • 5Analytics
  • Cognitivescale
  • Valohai Ltd
  • Determined AI
  • Datatron Technologies
  • DreamQuark
  • Acusense Technologie
  • MLPerf
  • Numericcal
  • Neptune Labs
  • IBM
  • Databricks
  • Iterative
  • Weights & Biases
  • ParallelM
  • Imandra
  • Peltarion
  • WidgetBrain

第十二章 附录

  • 相关调查
简介目录
Product Code: 60287

AI & Machine Learning Operationalization Software Market Size and Forecast

AI & Machine Learning Operationalization Software Market size was estimated at USD 6.12 Billion in 2024 and is projected to reach USD 36.25 Billion by 2032, growing at a CAGR of 35.2% from 2026 to 2032.

AI & Machine Learning Operationalization Software (MLOps software) streamlines the lifecycle of machine learning models, transitioning them from development to real-world applications.

By automating tasks like model deployment, monitoring, and governance, MLOps software ensures these models function effectively and reliably.

This translates to benefits like improved efficiency, reduced costs, and faster innovation cycles.

MLOps software empowers organizations to leverage the power of AI and machine learning for tasks like fraud detection, personalized recommendations, and predictive maintenance, ultimately driving significant business value.

Global AI & Machine Learning Operationalization Software Market Dynamics

The key market dynamics that are shaping the AI & machine learning operationalization software market include:

Key Market Drivers

Surging Adoption of AI & ML: The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) across various industries is driven primarily by the surge in demand. With AI and ML increasingly leveraged by organizations for tasks like automation, decision-making, and process optimization, there is a growing demand for MLOps software to effectively manage and operationalize these models.

Need for Streamlined Workflows: Streamlined workflows are necessitated by the complex nature of developing, deploying, and managing machine learning models. This need is fulfilled by MLOps software, which automates tasks such as model deployment, monitoring, and governance. The result of this automation is increased efficiency, reduced errors, and faster time-to-value for AI initiatives.

Growing Focus on Model Governance & Explainability: There is intensifying regulatory scrutiny surrounding AI and ML use, leading to a growing focus on model governance and explainability. MLOps software plays a crucial role in this regard by providing functionalities such as model governance and explainability. These features ensure compliance with regulations and enhance transparency in deployed models, thereby fostering trust and wider adoption.

Cloud Adoption & Scalability: Opportunities for MLOps software vendors are created by the burgeoning popularity of cloud computing. Scalability and cost-effectiveness are offered by cloud-based solutions, making them attractive options for organizations of all sizes. The growth of the MLOps software market is fueled by this shift towards cloud environments.

Key Challenges

Integration Complexity: Integrating MLOps software with existing enterprise systems can be a complex undertaking. Data silos, varying technology stacks, and a lack of standardization can create hurdles during implementation, hindering smooth operation.

Explainability and Trust: As regulations and ethical considerations around AI become more prominent, ensuring the explainability and trustworthiness of machine learning models is crucial. MLOps software needs to provide functionalities that demonstrate how models arrive at decisions, fostering trust and regulatory compliance.

Skilled Talent Shortage: The rapid growth of AI and ML has created a significant demand for skilled professionals with expertise in MLOps tools and methodologies. This talent shortage can limit the ability of organizations to effectively deploy and manage their MLOps infrastructure.

Key Trends

Surge in Automation: A rise in automation capabilities within MLOps software is being witnessed by the market. This includes tasks like model deployment, monitoring, and management being automated. Increased efficiency, reduced costs, and faster time-to-market for AI-powered solutions are translated by this.

Focus on Security and Explainability: Functionalities like model governance and explainability within MLOps software are being emphasized as regulations around AI and ML use tighten. Compliance, transparency, and responsible use of AI models deployed in real-world applications are ensured by these features.

Rise of Open-Source Options: Cost-effective alternatives for organizations are provided by the flourishing open-source MLOps community. Innovation is fostered, and accessibility to MLOps tools is widened by this. However, a significant market share is likely to be maintained by established vendors due to their comprehensive solutions and robust support services.

Global AI & Machine Learning Operationalization Software Market Regional Analysis

Here is a more detailed regional analysis of the AI & machine learning operationalization software market:

North America

Innovation in MLOps software in North America is fueled by a concentration of leading technology companies and a strong startup ecosystem.

Demand for MLOps solutions is driven by North American businesses, which are positioned at the forefront of AI and ML implementation due to a well-established culture of embracing cutting-edge technologies.

In the region, a highly skilled workforce in AI and related fields is fostered, providing the talent pool necessary for effectively developing and deploying MLOps software.

Significant investments in research and development (R&D) propel advancements in MLOps solutions within North America, solidifying their dominance in the market.

Europe

The development of MLOps software that emphasizes explainability, security, and compliance may be driven by Europe's strict regulations, such as GDPR, potentially granting European vendors a competitive advantage.

Talent and investment are being attracted to flourishing AI hubs in cities like London, Berlin, and Paris, fostering innovation in MLOps solutions tailored to European requirements.

The growth of domestic MLOps software companies could be stimulated by government initiatives supporting AI research and development in Europe, positioning them as formidable players in the market.

Global AI & Machine Learning Operationalization Software Market: Segmentation Analysis

The Global AI & Machine Learning Operationalization Software Market is Segmented Based on Application, Deployment, Functionality, End-Users, and Geography.

AI & Machine Learning Operationalization Software Market, By Application

  • Predictive Analytics
  • Natural Language Processing
  • Computer Vision
  • Speech Recognition
  • Anomaly Detection

Based on Application, the market is segmented into Predictive Analytics, Natural Language Processing, Computer Vision, Speech Recognition, and Anomaly Detection. Predictive Analytics holds the highest market share, attributed to the widespread adoption of predictive analytics across various industries, driving its dominance in the market.

AI & Machine Learning Operationalization Software Market, By Deployment

  • On-Premises
  • Cloud-Based
  • Hybrid

Based on Deployment, the market is bifurcated into On-Premises, Cloud-Based, and Hybrid. The cloud-based segment in the AI & Machine Learning Operationalization Software Market is currently experiencing the strongest growth. This is likely due to the increasing popularity of cloud computing and its advantages in scalability, cost-effectiveness, and easier management.

AI & Machine Learning Operationalization Software Market, By Functionality

  • Model Deployment & Management
  • Data Preprocessing & Feature Engineering
  • Model Monitoring & Performance Evaluation
  • Integration with Existing Systems

Based on Functionality, the market is classified into Model Deployment & Management, Data Preprocessing & Feature Engineering, Model Monitoring & Performance Evaluation, and Integration with Existing Systems. the highest market share is held by model deployment & management, determined by factors such as demand trends, industry requirements, and technological advancements.

AI & Machine Learning Operationalization Software Market, By End-Users

  • Healthcare
  • Finance
  • Retail
  • Manufacturing
  • Automotive
  • Government
  • Media & Entertainment
  • Telecommunications
  • Energy & Utilities
  • Education

Based on End-Users, the market is segmented into Healthcare, Finance, Retail, Manufacturing, Automotive, Government, Media & Entertainment, Telecommunications, Energy & Utilities, and Education. The highest market share is held by the healthcare sector, attributed to the adoption of AI and machine learning operationalization software for tasks such as patient diagnosis, personalized treatment plans, and medical imaging analysis.

AI & Machine Learning Operationalization Software Market, By Geography

  • North America
  • Europe
  • Asia Pacific
  • Rest of the World

Based on Geography, the AI & Machine Learning Operationalization Software Market is classified into North America, Europe, Asia Pacific, and the Rest of the world. The highest market share is held by North America, attributed to its strong foundation in technological innovation and a well-established ecosystem for AI adoption.

Key Players

  • The "AI & Machine Learning Operationalization Software Market" study report will provide valuable insight with an emphasis on the global market including some of the major players such as Algorithmia, Logical Clocks, Spell, 5Analytics, Cognitivescale, Valohai Ltd, Determined AI, Datatron Technologies, DreamQuark, Acusense Technologies, MLPerf, Numericcal, Neptune Labs, IBM, Databricks, Iterative, Weights & Biases, ParallelM, Imandra, Peltarion, and WidgetBrain.

Our market analysis includes a section specifically devoted to such major players, where our analysts give an overview of each player's financial statements, product benchmarking, and SWOT analysis. The competitive landscape section also includes key development strategies, market share analysis, and market positioning analysis of the players above globally.

  • AI & Machine Learning Operationalization Software Market Recent Developments
  • In June 2021, Determined AI was acquired by Hewlett Packard, thereby bolstering its AI and high-performance computing offerings with a robust MLOps platform. Through this acquisition, Hewlett Packard's position in the AI and high-performance computing space was strengthened by the addition of Determined AI's robust MLOps platform.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET

  • 1.1 Overview of the Market
  • 1.2 Scope of Report
  • 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

  • 3.1 Data Mining
  • 3.2 Validation
  • 3.3 Primary Interviews
  • 3.4 List of Data Sources

4 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET OUTLOOK

  • 4.1 Overview
  • 4.2 Market Dynamics
    • 4.2.1 Drivers
    • 4.2.2 Restraints
    • 4.2.3 Opportunities
  • 4.3 Porters Five Force Model
  • 4.4 Value Chain Analysis

5 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET, BY APPLICATION

  • 5.1 Predictive Analytics
  • 5.2 Natural Language Processing
  • 5.3 Computer Vision
  • 5.4 Speech Recognition
  • 5.5 Anomaly Detection

6 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET, BY Deployment

  • 6.1 On-Premises
  • 6.2Cloud-Based
  • 6.3Hybrid

7 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET, BY Functionality

  • 7.1 Model Deployment & Management
  • 7.2 Data Preprocessing & Feature Engineering
  • 7.3 Model Monitoring & Performance Evaluation
  • 7.4 Integration with Existing Systems

8 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET, BY End-Users

  • 8.1 Healthcare
  • 8.2 Finance
  • 8.3 Retail
  • 8.4 Manufacturing
  • 8.5 Automotive
  • 8.6 Government
  • 8.7 Media & Entertainment
  • 8.8 Telecommunications
  • 8.9 Energy & Utilities
  • 8.10 Education

9 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET, BY GEOGRAPHY

  • 9.1 Overview
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 U.K.
    • 9.3.3 France
    • 9.3.4 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 Japan
    • 9.4.3 India
    • 9.4.4 Rest of Asia Pacific
  • 9.5 Rest of the World
    • 9.5.1 Latin America
    • 9.5.2 Middle East & Africa

10 GLOBAL AI AND MACHINE LEARNING OPERATIONALIZATION SOFTWARE MARKET COMPETITIVE LANDSCAPE

  • 10.1 Overview
  • 10.2 Company Market Ranking
  • 10.3 Key Development Strategies

11 COMPANY PROFILES

  • 11.1 Algorithmia
    • 11.1.1 Overview
    • 11.1.2 Financial Performance
    • 11.1.3 Product Outlook
    • 11.1.4 Key Developments
  • 11.2 Logical Clocks
    • 11.2.1 Overview
    • 11.2.2 Financial Performance
    • 11.2.3 Product Outlook
    • 11.2.4 Key Developments
  • 11.3 Spell
    • 11.3.1 Overview
    • 11.3.2 Financial Performance
    • 11.3.3 Product Outlook
    • 11.3.4 Key Developments
  • 11.4 5Analytics
    • 11.4.1 Overview
    • 11.4.2 Financial Performance
    • 11.4.3 Product Outlook
    • 11.4.4 Key Developments
  • 11.5 Cognitivescale
    • 11.5.1 Overview
    • 11.5.2 Financial Performance
    • 11.5.3 Product Outlook
    • 11.5.4 Key Developments
  • 11.6 Valohai Ltd
    • 11.6.1 Overview
    • 11.6.2 Financial Performance
    • 11.6.3 Product Outlook
    • 11.6.4 Key Developments
  • 11.7 Determined AI
    • 11.7.1 Overview
    • 11.7.2 Financial Performance
    • 11.7.3 Product Outlook
    • 11.7.4 Key Developments
  • 11.8 Datatron Technologies
    • 11.8.1 Overview
    • 11.8.2 Financial Performance
    • 11.8.3 Product Outlook
    • 11.8.4 Key Developments
  • 11.9 DreamQuark
  • 11.10 Acusense Technologie
  • 11.11MLPerf
  • 11.12 Numericcal
  • 11.13Neptune Labs
  • 11.14IBM
  • 11.15Databricks
  • 11.16 Iterative
  • 11.17 Weights & Biases
  • 11.18 ParallelM
  • 11.19Imandra
  • 11.20Peltarion
  • 11.21WidgetBrain

12 Appendix

  • 12.1 Related Research