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AI・機械学習運用ソフトウェアの世界市場：用途別、展開別、機能別、エンドユーザー別、地域別範囲と予測

Global AI & Machine Learning Operationalization Software Market By Application, By Deployment, By Functionality, By End-User, & By Geographic Scope And Forecast

出版日
ページ情報
英文 202 Pages
納期
2～3営業日
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価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=144.76円
AI・機械学習運用ソフトウェアの世界市場：用途別、展開別、機能別、エンドユーザー別、地域別範囲と予測
出版日: 2025年05月12日
発行: Verified Market Research
ページ情報: 英文 202 Pages
納期: 2～3営業日
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  • 概要
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概要

AI・機械学習運用化ソフトウェア市場規模と予測

AI・機械学習運用化ソフトウェア市場規模は、2024年に61億2,000万米ドルと推定され、2026～2032年にかけて35.2%のCAGRで成長し、2032年には362億5,000万米ドルに達すると予測されます。

AI・機械学習運用化ソフトウェア（MLOpsソフトウェア）は、機械学習モデルのライフサイクルを合理化し、開発から実世界への応用へと移行させています。

モデルの展開、モニタリング、ガバナンスなどのタスクを自動化することで、MLOpsソフトウェアはこれらのモデルが効果的かつ確実に機能することを保証します。

これは、効率性の向上、コストの削減、イノベーション・サイクルの迅速化などのメリットにつながります。

MLOpsソフトウェアは、不正検知、パーソナライズされたレコメンデーション、予測メンテナンスなどのタスクにAIと機械学習のパワーを活用することで、最終的に大きなビジネス価値を促進します。

世界のAI・機械学習運用化ソフトウェア市場力学

AI・機械学習運用化ソフトウェア市場を形成している主要市場力学は以下の通りです。

主要市場促進要因

AIとMLの採用急増：さまざまな産業で人工知能（AI）と機械学習（ML）が広く採用されているのは、主に需要の急増によるものです。自動化、意思決定、プロセス最適化などのタスクにAIとMLを活用する企業が増えており、これらのモデルを効果的に管理・運用するMLOpsソフトウェアに対する需要が高まっています。

合理化されたワークフローの必要性：機械学習モデルの開発、導入、管理は複雑なため、合理化されたワークフローが必要です。このニーズを満たすのが、モデルのデプロイ、モニタリング、ガバナンスなどのタスクを自動化するMLOpsソフトウェアです。この自動化の結果、効率が向上し、エラーが減少し、AIイニシアチブの価値実現までの時間が短縮されます。

モデル・ガバナンスと説明可能性への注目の高まり：AIとMLの利用をめぐる規制当局のモニタリングが強化され、モデルのガバナンスと説明可能性への注目が高まっています。MLOpsソフトウェアは、モデルガバナンスや説明可能性などの機能を提供することで、この点で重要な役割を果たしています。これらの機能は、規制へのコンプライアンスを確保し、導入されたモデルの透明性を高めることで、信頼と幅広い採用を促進します。

クラウドの採用と拡大性：MLOpsソフトウェアベンダーにとってのビジネス機会は、クラウドコンピューティングの急速な普及によってもたらされます。クラウドベースのソリューションが提供するスケーラビリティとコスト効率は、あらゆる規模の組織にとって魅力的な選択肢となっています。MLOpsソフトウェア市場の成長は、このようなクラウド環境へのシフトによって促進されます。

主要課題

統合の複雑さ：MLOpsソフトウェアと既存の企業システムとの統合は、複雑な作業になる可能性があります。データのサイロ化、さまざまな技術・スタック、標準化の欠如などが、導入時の障害となり、スムーズな運用を妨げます。

説明可能性と信頼性：AIに関する規制や倫理的配慮が顕著になるにつれ、機械学習モデルの説明可能性と信頼性を確保することが極めて重要になっています。MLOpsソフトウェアは、モデルがどのように意思決定に至ったかを示す機能を提供し、信頼と規制遵守を促進する必要があります。

スキルのある人材の不足：AIとMLの急速な成長により、MLOpsのツールや手法に精通した熟練した専門家に対する大きな需要が生まれています。この人材不足は、組織がMLOpsインフラを効果的に展開・管理する能力を制限する可能性があります。

主要動向

自動化の急増：MLOpsソフトウェアにおける自動化機能の増加が市場で確認されています。これには、モデルのデプロイ、モニタリング、管理といったタスクの自動化が含まれます。AIを活用したソリューションの効率性向上、コスト削減、市場投入までの時間短縮は、このことを意味します。

セキュリティと説明可能性の重視：AIやMLの利用に関する規制が強化されるにつれ、MLOpsソフトウェアにおけるモデルガバナンスや説明可能性といった機能が重視されています。コンプライアンス、透明性、実際のアプリケーションに導入されたAIモデルの責任ある使用は、これらの機能によって保証されます。

オープンソースオプションの台頭：オープンソースのMLOpsコミュニティが繁栄することで、企業にとって費用対効果の高い選択肢が提供されます。イノベーションが促進され、MLOpsツールへのアクセシビリティが広がります。しかし、包括的なソリューションと強固なサポートサービスにより、既存ベンダーが大きな市場シェアを維持する可能性は高いです。

目次

第1章 AI・機械学習運用化ソフトウェアの世界市場のイントロダクション

  • 市場概要
  • 調査範囲
  • 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

  • データマイニング
  • バリデーション
  • 一次資料
  • データソース一覧

第4章 AI・機械学習運用化ソフトウェアの世界市場展望

  • 概要
  • 市場力学
    • 促進要因
    • 抑制要因
    • 機会
  • ポーターのファイブフォースモデル
  • バリューチェーン分析

第5章 AI・機械学習運用化ソフトウェアの世界市場：用途別

  • 予測分析
  • 自然言語処理
  • コンピュータビジョン
  • 音声認識
  • 異常検知

第6章 AI・機械学習運用化ソフトウェアの世界市場：展開別

  • オンプレミス
  • クラウドベース
  • ハイブリッド

第7章 AI・機械学習運用化ソフトウェアの世界市場：機能別

  • モデルの展開と管理
  • データ前処理とフィーチャーエンジニアリング
  • モデルのモニタリングとパフォーマンス評価
  • 既存システムとの統合

第8章 AI・機械学習運用化ソフトウェアの世界市場：エンドユーザー別

  • ヘルスケア
  • 金融
  • 小売
  • 製造業
  • 自動車
  • 政府機関
  • メディア娯楽
  • 通信
  • エネルギー公益事業
  • 教育

第9章 AI・機械学習運用化ソフトウェアの世界市場：地域別

  • 概要
  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • その他の欧州
  • アジア太平洋
    • 中国
    • 日本
    • インド
    • その他のアジア太平洋
  • その他
    • ラテンアメリカ
    • 中東・アフリカ

第10章 世界のAI・機械学習運用化ソフトウェア市場の競合情勢

  • 概要
  • 各社の市場ランキング
  • 主要開発戦略

第11章 企業プロファイル

  • 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12章 付録

  • 関連調査
目次
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