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2030 年 MLOps 市場預測：按組件、部署、公司類型、應用程式、最終用戶和地區進行的全球分析

MLOps Market Forecasts to 2030 - Global Analysis By Component (Platform and Service), Deployment (Cloud, On-premise and Hybrid), Enterprise Type, Application, End User and by Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，2024 年全球 MLOps 市場規模為 14.416 億美元，預計到 2030 年將達到 115.7135 億美元，預測期內複合年成長率為 41.5%。

MLOps（機器學習營運）是一個結合了資料工程、DevOps 和機器學習技術的領域，旨在簡化和擴展生產環境中機器學習模型的部署、監控和管理。 MLOps 提供模型的持續整合、測試和交付，使組織能夠更快速、更可靠地大規模部署模型。此外，企業可以實施 MLOps 來減少操作摩擦，透過持續學習提高模型準確性，並確保機器學習 (ML) 模型在條件變更時保持適用和有用。

根據國際資料公司 (IDC) 的數據，在機器學習的進步和各行業擴大採用人工智慧的推動下，全球人工智慧系統支出預計到 2023 年將達到 979 億美元。

擴大人工智慧和機器學習的使用

推動 MLOps 市場的關鍵因素之一是人工智慧和機器學習在製造、金融、醫療保健和零售等領域的廣泛使用。公司正在大力投資開發和實施機器學習模型，認知到人工智慧在產生業務洞察、最佳化流程和改善客戶體驗方面的潛力。此外，將人工智慧融入當前業務流程的難度以及管理大量資料的需求使得強大的 MLOps 平台變得越來越必要。

實施成本過高

MLOps 解決方案的高實施成本是阻礙 MLOps 市場成長的主要因素之一。開發和實施全面的 MLOps 框架需要對基礎設施、工具和人員進行大量投資。為了在整個生命週期中管理機器學習模型，公司通常需要投資雲端服務、高效能運算資源和複雜的軟體工具。此外，對於小型企業和預算有限的公司來說，這些成本可能令人望而卻步，並阻止他們全面實施 MLOps 解決方案。

基礎設施即服務 (IaaS) 和雲端運算的成長

基礎設施即服務 (IaaS) 和雲端運算產業正在快速發展，為 MLOps 創造了新的市場機會。機器學習模型的開發、部署和管理由 AWS、Google Cloud 和 Microsoft Azure 等雲端平台提供的可擴展且適應性強的基礎架構提供支援。此外，雲端基礎的解決方案的日益普及降低了管理硬體和軟體資源的複雜性和成本，同時允許企業利用 MLOps 的優勢，例如自動化模型部署和持續監控。

市場飽和，競爭加劇

更多成熟的科技公司和新興企業正在進入 MLOps 市場，加劇了競爭。競爭者眾多的市場飽和使得個別 MLOps 提供者很難在競爭中脫穎而出並獲得市場佔有率。為了保持競爭力，營運商可能被迫提供更先進的功能或降低成本，這可能會影響永續性和盈利。此外，過多的不同 MLOps 解決方案可能會讓潛在客戶感到困惑，並使其難以選擇最適合其獨特需求的解決方案。

COVID-19 的影響：

由於 COVID-19 的爆發，機器學習和人工智慧 (AI) 技術在各個行業中變得越來越流行。公司需要最佳化業務並適應快速變化的環境。遠端工作的增加、對數位平台的依賴增加以及對資料驅動洞察力的迫切需求增加了對能夠有效管理和大規模部署機器學習模型的 MLOps 解決方案的需求。然而，疫情也暴露了現有基礎設施的弱點，並使 MLOps 框架的擴充性和安全性問題成為人們關注的焦點。

預計平台部分在預測期內將是最大的

在 MLOps 市場中，平台部分佔據最大佔有率。模型開發、部署和監控均由 MLOps 平台提供，該平台透過全套工具和服務簡化了機器學習生命週期。這些平台提供版本控制、協作工具和自動化模型訓練等關鍵功能，可提高組織的效率和擴充性。此外，這些平台尋求有效利用人工智慧技術，透過將機器學習工作流程的各個階段整合到對企業至關重要的單一系統中，促進機器學習模型的快​​速可靠部署。

雲細分市場預計在預測期內複合年成長率最高

MLOps 市場的雲端部分正以最高的複合年成長率成長。雲端基礎的MLOps 解決方案具有極高的成本效益、擴充性和靈活性。這些解決方案允許企業利用雲端基礎架構來管理和部署機器學習模型，而無需大量投資本地硬體。雲端環境有利於輕鬆協作、動態資源分配以及與其他雲端基礎的服務的無縫整合，所有這些都加速了機器學習模型的創建和應用。此外，隨著越來越多的公司利用雲端技術來提高資料處理能力和自動化人工智慧業務，對雲端基礎的MLOps 解決方案的需求正在迅速增加。

佔比最大的地區：

北美地區預計將佔據 MLOps 市場的最大佔有率。該地區強大的技術基礎設施、頂尖科技公司的集中以及對機器學習和人工智慧計劃的大量投資是該地區優勢的主要原因。北美的主導地位是 MLOps解決方案供應商的發達生態系統以及對創新和研究的關注的結果。此外，該地區主要資料中心和雲端服務供應商的存在也促進了 MLOps 實踐的擴散，使北美處於行業的前沿。

複合年成長率最高的地區：

MLOps 市場複合年成長率最高的地區是亞太地區。該地區不斷發展的數位基礎設施、人工智慧技術的日益使用以及公共和私人對機器學習和資料分析的快速投資正在促進該地區的快速成長。中國、印度和日本等國家在技術創新和進步方面處於領先地位。此外，該地區快速發展的高科技新興企業以及對跨行業數位轉型的日益重視也推動了對 MLOps 解決方案的需求。

免費客製化服務：

訂閱此報告的客戶可以存取以下免費自訂選項之一：

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  • 區域分割
    • 根據客戶興趣對主要國家的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭標基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二次研究資訊來源
    • 先決條件

第3章市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第4章波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第 5 章：全球 MLOps 市場：按組成部分

  • 平台
  • 服務

第 6 章 MLOps 的全球市場：依部署分類

  • 雲
  • 本地
  • 混合

第 7 章：全球 MLOps 市場：依公司類型

  • 小型企業
  • 主要企業

第8章全球 MLOps 市場：依應用分類

  • 資料管理
  • 基礎設施模型
  • 其他

第 9 章：全球 MLOps 市場：依最終使用者分類

  • 資訊科技/通訊
  • 醫療保健/生命科學
  • 銀行、金融服務和保險
  • 製造業
  • 零售
  • 政府和公共部門
  • 廣告
  • 運輸/物流
  • 能源和公共
  • 其他

第 10 章全球MLOps 市場：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東/非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東/非洲

第11章 主要進展

  • 合約、夥伴關係、合作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章 公司概況

  • Google LLC
  • Allegro AI.
  • Domino Data Lab, Inc.
  • Cognizant
  • GAVS Technologies
  • Amazon Web Services Inc.
  • Databricks, Inc.
  • IBM Corporation
  • Cloudera, Inc
  • Microsoft Corporation
  • Hewlett Packard Enterprise Development LP
  • Alteryx
  • Valohai
  • DataRobot, Inc.
  • Neptune Labs Inc.
Product Code: SMRC27099

According to Stratistics MRC, the Global MLOps Market is accounted for $1441.60 million in 2024 and is expected to reach $11571.35 million by 2030 growing at a CAGR of 41.5% during the forecast period. MLOps, or Machine Learning Operations, is a field that streamlines and scales the deployment, monitoring, and management of machine learning models in production environments by fusing data engineering, DevOps, and machine learning techniques. Organizations can more quickly and reliably deploy models at scale owing to MLOps continuous integration, testing, and delivery of models. Moreover, businesses may lower operational friction, improve model accuracy through ongoing learning, and make sure their machine learning (ML) models stay applicable and useful in changing conditions by putting MLOps into practice.

According to the International Data Corporation (IDC), global spending on artificial intelligence systems is expected to reach $97.9 billion in 2023, driven by advancements in machine learning and the growing adoption of AI across various industries.

Market Dynamics:

Driver:

Growing use of AI and machine learning

One of the main factors propelling the MLOps market is the extensive use of AI and machine learning in sectors like manufacturing, finance, healthcare, and retail. Businesses are investing extensively in developing and implementing machine learning models as they realize the potential of AI to generate business insights, optimize processes, and improve customer experiences. Additionally, strong MLOps platforms are becoming more and more necessary due to the difficulty of incorporating AI into current business processes and the requirement to manage massive volumes of data.

Restraint:

Exorbitant implementation expenses

The high cost of implementing MLOps solutions is one of the major factors impeding the growth of the MLOps market. It takes a significant investment in infrastructure, tools, and talent to develop and implement an all-encompassing MLOps framework. To manage machine learning models throughout their entire lifecycle, organizations frequently need to invest in cloud services, high-performance computing resources, and sophisticated software tools. Furthermore, these expenses might be unaffordable for smaller businesses or those with tighter budgets, which would prevent them from fully implementing MLOps solutions.

Opportunity:

Growth of infrastructure-as-a-service (IaaS) and cloud computing

The infrastructure-as-a-service (IaaS) and cloud computing industries are growing quickly, which is opening up new market opportunities for MLOps. Machine learning model development, deployment, and management are supported by scalable and adaptable infrastructure provided by cloud platforms like AWS, Google Cloud, and Microsoft Azure. Moreover, the growing popularity of cloud-based solutions lowers the complexity and expense of managing hardware and software resources while enabling enterprises to take advantage of MLOps advantages, like automated model deployment and continuous monitoring.

Threat:

Growing market saturation and competition

A growing number of well-established tech companies and startups are entering the MLOps market, making it more competitive. Due to market saturation caused by this flood of competitors, it is harder for individual MLOps providers to stand out from the competition and take market share. In order to stay competitive, businesses may feel pressure to provide more sophisticated features or reduce costs, which could have an effect on sustainability and profitability. Additionally, the abundance of different MLOps solutions may confuse prospective clients, making it difficult for them to choose the one that best suits their unique requirements.

Covid-19 Impact:

Machine learning and artificial intelligence (AI) technologies have become increasingly popular in a variety of industries due to the COVID-19 pandemic. This is because businesses needed to optimize their operations and adjust to rapidly changing conditions. The demand for MLOps solutions that could effectively manage and deploy machine learning models at scale increased due to the rise in remote work, increased reliance on digital platforms, and the pressing need for data-driven insights. However, the pandemic also revealed weaknesses in the infrastructure that was already in place and brought attention to issues with scaling and securing MLOps frameworks.

The Platform segment is expected to be the largest during the forecast period

The platform segment has the largest share in the MLOps market. Model development, deployment, and monitoring are all streamlined in the machine learning lifecycle by the full range of tools and services provided by MLOps platforms. These platforms offer crucial features that improve an organization's efficiency and scalability, like version control, collaboration tools, and automated model training. Furthermore, these platforms are essential for companies looking to effectively use AI technology because they facilitate the faster and more dependable deployment of machine learning models by combining different phases of the ML workflow into a single system.

The Cloud segment is expected to have the highest CAGR during the forecast period

The cloud segment of the MLOps market is growing at the highest CAGR. Cloud-based MLOps solutions are very advantageous in terms of cost-effectiveness, scalability, and flexibility. With the help of these solutions, businesses can use cloud infrastructure to manage and deploy machine learning models without having to make significant investments in on-premise hardware. The cloud environment facilitates easy collaboration, dynamic resource allocation, and seamless integration with other cloud-based services, all of which speed up the creation and application of machine learning models. Moreover, the demand for cloud-based MLOps solutions is growing quickly as more companies use cloud technologies to improve their data processing capabilities and automate their AI operations.

Region with largest share:

The North American region is anticipated to hold the largest share of the MLOps market. The region's strong technological infrastructure, concentration of top technology companies, and large investments in machine learning and artificial intelligence projects are the main causes of its dominance. North America's dominant position is a result of its developed ecosystem of MLOps solution providers as well as its strong emphasis on innovation and research. Additionally, major data centers and cloud service providers are also present in the area, which encourages the widespread adoption of MLOps practices and puts North America at the forefront of the industry.

Region with highest CAGR:

The MLOps market is growing at the highest CAGR in the Asia-Pacific region. The region's growing digital infrastructure, rising use of AI technologies, and a spike in investments in machine learning and data analytics from the public and private sectors are all contributing to its rapid growth. Leading the way in technological innovation and advancement are nations like China, India, and Japan. Furthermore, the demand for MLOps solutions is being driven by the region's burgeoning tech startups and growing emphasis on digital transformation across various industries.

Key players in the market

Some of the key players in MLOps market include Google LLC, Allegro AI., Domino Data Lab, Inc., Cognizant, GAVS Technologies, Amazon Web Services Inc., Databricks, Inc., IBM Corporation, Cloudera, Inc, Microsoft Corporation, Hewlett Packard Enterprise Development LP, Alteryx, Valohai, DataRobot, Inc. and Neptune Labs Inc.

Key Developments:

In August 2024, Amazon has reached an agreement to acquire chip maker and AI model compression company Perceive, a San Jose, Calif.-based subsidiary of publicly traded technology company Xperi, for $80 million in cash. The deal was disclosed Friday afternoon in a filing by Xperi with the Securities and Exchange Commission.

In May 2024, Google LLC has entered into power purchase agreements (PPAs) with two Japanese energy providers securing 60 MW of solar capacity dedicated to providing electricity to the company's data centres in Japan. The tech giant said the PPAs, the first of their kind for Google in the country, were signed with Clean Energy Connect Inc, a partner of Itochu Corp (TYO:8001), and Shizen Energy.

In August 2023, Allegro MicroSystems announced it has signed a definitive agreement to acquire Crocus Technology, a developer of magnetic sensors based on tunnel-magnetoresistance (TMR) technology. The transaction amounts to $420 million and will be paid in cash. Crocus was spun off from Grenoble, France-based research laboratory in spintronics Spintec in 2006.

Components Covered:

  • Platform
  • Service

Deployments Covered:

  • Cloud
  • On-premise
  • Hybrid

Enterprise Types Covered:

  • SMEs
  • Large Enterprises

Applications Covered:

  • Data Management
  • Model Infrastructure
  • Other Applications

End Users Covered:

  • IT & Telecom
  • Healthcare and Life Sciences
  • Banking, Financial Services, and Insurance
  • Manufacturing
  • Retail
  • Government & Public Sector
  • Advertising
  • Transportation and Logistics
  • Energy and Utilities
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global MLOps Market, By Component

  • 5.1 Introduction
  • 5.2 Platform
  • 5.3 Service

6 Global MLOps Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud
  • 6.3 On-premise
  • 6.4 Hybrid

7 Global MLOps Market, By Enterprise Type

  • 7.1 Introduction
  • 7.2 SMEs
  • 7.3 Large Enterprises

8 Global MLOps Market, By Application

  • 8.1 Introduction
  • 8.2 Data Management
  • 8.3 Model Infrastructure
  • 8.4 Other Applications

9 Global MLOps Market, By End User

  • 9.1 Introduction
  • 9.2 IT & Telecom
  • 9.3 Healthcare and Life Sciences
  • 9.4 Banking, Financial Services, and Insurance
  • 9.5 Manufacturing
  • 9.6 Retail
  • 9.7 Government & Public Sector
  • 9.8 Advertising
  • 9.9 Transportation and Logistics
  • 9.10 Energy and Utilities
  • 9.11 Other End Users

10 Global MLOps Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Google LLC
  • 12.2 Allegro AI.
  • 12.3 Domino Data Lab, Inc.
  • 12.4 Cognizant
  • 12.5 GAVS Technologies
  • 12.6 Amazon Web Services Inc.
  • 12.7 Databricks, Inc.
  • 12.8 IBM Corporation
  • 12.9 Cloudera, Inc
  • 12.10 Microsoft Corporation
  • 12.11 Hewlett Packard Enterprise Development LP
  • 12.12 Alteryx
  • 12.13 Valohai
  • 12.14 DataRobot, Inc.
  • 12.15 Neptune Labs Inc.

List of Tables

  • Table 1 Global MLOps Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global MLOps Market Outlook, By Component (2022-2030) ($MN)
  • Table 3 Global MLOps Market Outlook, By Platform (2022-2030) ($MN)
  • Table 4 Global MLOps Market Outlook, By Service (2022-2030) ($MN)
  • Table 5 Global MLOps Market Outlook, By Deployment (2022-2030) ($MN)
  • Table 6 Global MLOps Market Outlook, By Cloud (2022-2030) ($MN)
  • Table 7 Global MLOps Market Outlook, By On-premise (2022-2030) ($MN)
  • Table 8 Global MLOps Market Outlook, By Hybrid (2022-2030) ($MN)
  • Table 9 Global MLOps Market Outlook, By Enterprise Type (2022-2030) ($MN)
  • Table 10 Global MLOps Market Outlook, By SMEs (2022-2030) ($MN)
  • Table 11 Global MLOps Market Outlook, By Large Enterprises (2022-2030) ($MN)
  • Table 12 Global MLOps Market Outlook, By Application (2022-2030) ($MN)
  • Table 13 Global MLOps Market Outlook, By Data Management (2022-2030) ($MN)
  • Table 14 Global MLOps Market Outlook, By Model Infrastructure (2022-2030) ($MN)
  • Table 15 Global MLOps Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 16 Global MLOps Market Outlook, By End User (2022-2030) ($MN)
  • Table 17 Global MLOps Market Outlook, By IT & Telecom (2022-2030) ($MN)
  • Table 18 Global MLOps Market Outlook, By Healthcare and Life Sciences (2022-2030) ($MN)
  • Table 19 Global MLOps Market Outlook, By Banking, Financial Services, and Insurance (2022-2030) ($MN)
  • Table 20 Global MLOps Market Outlook, By Manufacturing (2022-2030) ($MN)
  • Table 21 Global MLOps Market Outlook, By Retail (2022-2030) ($MN)
  • Table 22 Global MLOps Market Outlook, By Government & Public Sector (2022-2030) ($MN)
  • Table 23 Global MLOps Market Outlook, By Advertising (2022-2030) ($MN)
  • Table 24 Global MLOps Market Outlook, By Transportation and Logistics (2022-2030) ($MN)
  • Table 25 Global MLOps Market Outlook, By Energy and Utilities (2022-2030) ($MN)
  • Table 26 Global MLOps Market Outlook, By Other End Users (2022-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.