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市場調查報告書

商品編碼

1738534

全球人工智慧和機器學習營運軟體市場：按應用、部署、功能、最終用戶和地區分類的範圍和預測

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: 2025年05月12日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

人工智慧/機器學習操作化軟體市場規模與預測

人工智慧和機器學習操作化軟體市場規模預計在 2024 年達到 61.2 億美元，預計在 2026 年至 2032 年期間的複合年成長率為 35.2%，到 2032 年將達到 362.5 億美元。

人工智慧和機器學習操作化軟體（MLOps 軟體）正在簡化機器學習模型的生命週期，將其從開發轉移到實際應用。

透過自動執行模型部署、監控和管治等任務，MLOps 軟體可確保這些模型有效可靠地運作。

這意味著效率提高、成本降低和創新週期加快等好處。

MLOps 軟體最終透過利用人工智慧和機器學習的力量來完成詐欺偵測、個人化建議和預測性維護等任務，從而實現巨大的商業價值。

全球人工智慧和機器學習操作化軟體市場動態

影響人工智慧和機器學習操作化軟體市場的關鍵市場動態是：

關鍵市場促進因素

人工智慧 (AI) 和機器學習 (ML) 應用激增：人工智慧 (AI) 和機器學習 (ML) 在各行各業的廣泛應用，主要源自於需求激增。越來越多的企業利用人工智慧和機器學習來實現自動化、決策和流程最佳化等任務，這推動了對 MLOps 軟體的需求，以便有效地管理和操作這些模型。

需要簡化的工作流程：開發、部署和管理機器學習模型的複雜性需要簡化的工作流程。 MLOps 軟體可以滿足這項需求，它可以自動執行模型部署、監控和管治等任務。這種自動化可以提高效率、減少錯誤，並加快 AI舉措的價值實現時間。

更重視模型管治和可解釋性：圍繞人工智慧和機器學習應用的監管審查日益嚴格，導致人們更加關注模型管治和可解釋性。 MLOps 軟體透過提供模型管治和可解釋性等功能，在這方面發揮關鍵作用。這些功能可確保合規性，並提高已部署模型的透明度，從而增強信任並促進更廣泛的採用。

雲端採用和可擴展性：雲端運算的快速普及為 MLOps 軟體供應商帶來了機會。雲端基礎方案提供的可擴展性和成本效益使其成為各種規模組織的理想選擇。 MLOps 軟體市場的成長正源自於這種向雲端環境的轉變。

主要問題

整合複雜性：將 MLOps 軟體與現有企業系統整合可能是一項複雜的任務。資料孤島、不同的技術堆疊以及缺乏標準化會在實施過程中造成障礙，妨礙順利運作。

可解釋性和可信度：隨著圍繞人工智慧的監管和倫理考慮日益凸顯，確保機器學習模型的可解釋性和可信度至關重要。 MLOps 軟體必須能夠展示模型如何得出決策，從而提升信任度並促進合規性。

技能人才短缺：人工智慧和機器學習的快速發展，對精通 MLOps 工具和技術的技能型專業人員的需求龐大。這種人才短缺可能會限制組織有效部署和管理其 MLOps 基礎設施的能力。

主要趨勢

自動化浪潮：市場見證了 MLOps 軟體自動化功能的不斷提升。這包括模型部署、監控和管理等任務的自動化。這意味著 AI 解決方案的效率提升、成本降低和上市縮短時間。

專注於安全性和可解釋性：有關人工智慧和機器學習使用的法規日益增多，推動了對 MLOps 軟體中模型管治和可解釋性等功能的重視，這些功能可確保在實際應用中部署的人工智慧模型的合規性、透明度和負責任的使用。

開放原始碼選項的興起：蓬勃發展的開放原始碼MLOps 社群為企業提供了經濟高效的選擇，促進了創新並擴大了 MLOps 工具的可及性。然而，現有供應商預計將憑藉全面的解決方案和可靠的支援服務，繼續保持相當大的市場佔有率。

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.

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