查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1551316

2030 年 MLOps 市场预测：按组件、部署、公司类型、应用程式、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，2024 年全球 MLOps 市场规模为 14.416 亿美元，预计到 2030 年将达到 115.7135 亿美元，预测期内复合年增长率为 41.5%。

MLOps（机器学习营运）是一个结合了资料工程、DevOps 和机器学习技术的领域，旨在简化和扩展生产环境中机器学习模型的部署、监控和管理。 MLOps 提供模型的持续整合、测试和交付，使组织能够更快速、更可靠地大规模部署模型。此外，企业可以实施 MLOps 来减少操作摩擦，透过持续学习提高模型准确性，并确保机器学习 (ML) 模型在条件变更时保持适用和有用。

根据国际资料公司 (IDC) 的数据，在机器学习的进步和各行业越来越多地采用人工智慧的推动下，全球人工智慧系统支出预计到 2023 年将达到 979 亿美元。

扩大人工智慧和机器学习的使用

推动 MLOps 市场的关键因素之一是人工智慧和机器学习在製造、金融、医疗保健和零售等领域的广泛使用。公司正在大力投资开发和实施机器学习模型，认识到人工智慧在产生业务洞察、优化流程和改善客户体验方面的潜力。此外，将人工智慧融入当前业务流程的难度以及管理大量资料的需求使得强大的 MLOps 平台变得越来越必要。

实施成本过高

MLOps 解决方案的高实施成本是阻碍 MLOps 市场成长的主要因素之一。开发和实施全面的 MLOps 框架需要对基础设施、工具和人员进行大量投资。为了在整个生命週期中管理机器学习模型，公司通常需要投资云端服务、高效能运算资源和复杂的软体工具。此外，对于小型企业和预算有限的公司来说，这些成本可能令人望而却步，并阻止他们全面实施 MLOps 解决方案。

基础设施即服务 (IaaS) 和云端运算的成长

基础设施即服务 (IaaS) 和云端运算产业正在快速发展，为 MLOps 创造了新的市场机会。机器学习模型的开发、部署和管理由 AWS、Google Cloud 和 Microsoft Azure 等云端平台提供的可扩展且适应性强的基础架构提供支援。此外，云端基础的解决方案的日益普及降低了管理硬体和软体资源的复杂性和成本，同时允许企业利用 MLOps 的优势，例如自动化模型部署和持续监控。

市场饱和，竞争加剧

更多成熟的科技公司和新兴企业正在进入 MLOps 市场，加剧了竞争。竞争者众多的市场饱和使得个别 MLOps 提供者很难在竞争中脱颖而出并获得市场占有率。为了保持竞争力，营运商可能被迫提供更先进的功能或降低成本，这可能会影响永续性和盈利。此外，过多的不同 MLOps 解决方案可能会让潜在客户感到困惑，并使其难以选择最适合其独特需求的解决方案。

COVID-19 的影响：

由于 COVID-19 的爆发，机器学习和人工智慧 (AI) 技术在各个行业中变得越来越流行。公司需要优化业务并适应快速变化的环境。远端工作的增加、对数位平台的依赖增加以及对资料驱动洞察力的迫切需求增加了对能够有效管理和大规模部署机器学习模型的 MLOps 解决方案的需求。然而，疫情也暴露了现有基础设施的弱点，并使 MLOps 框架的扩充性和安全性问题成为人们关注的焦点。

预计平台部分在预测期内将是最大的

在 MLOps 市场中，平台部分占据最大份额。模型开发、部署和监控均由 MLOps 平台提供，该平台透过全套工具和服务简化了机器学习生命週期。这些平台提供版本控制、协作工具和自动化模型训练等关键功能，可提高组织的效率和扩充性。此外，这些平台寻求有效利用人工智慧技术，透过将机器学习工作流程的各个阶段整合到对企业至关重要的单一系统中，促进机器学习模型的快​​速可靠部署。

云细分市场预计在预测期内复合年增长率最高

MLOps 市场的云端部分正以最高的复合年增长率成长。云端基础的MLOps 解决方案具有极高的成本效益、扩充性和灵活性。这些解决方案允许企业利用云端基础架构来管理和部署机器学习模型，而无需大量投资本地硬体。云端环境有利于轻鬆协作、动态资源分配以及与其他云端基础的服务的无缝集成，所有这些都加速了机器学习模型的创建和应用。此外，随着越来越多的公司利用云端技术来提高资料处理能力和自动化人工智慧业务，对云端基础的MLOps 解决方案的需求正在迅速增加。

占比最大的地区：

北美地区预计将占据 MLOps 市场的最大份额。该地区强大的技术基础设施、顶尖科技公司的集中以及对机器学习和人工智慧计划的大量投资是该地区优势的主要原因。北美的主导地位是 MLOps解决方案供应商的发达生态系统以及对创新和研究的关注的结果。此外，该地区主要资料中心和云端服务供应商的存在也促进了 MLOps 实践的扩散，使北美处于行业的前沿。

复合年增长率最高的地区：

MLOps 市场复合年增长率最高的地区是亚太地区。该地区不断发展的数位基础设施、人工智慧技术的日益使用以及公共和私人对机器学习和资料分析的快速投资正在促进该地区的快速增长。中国、印度和日本等国家在技术创新和进步方面处于领先地位。此外，该地区快速发展的高科技新兴企业以及对跨行业数位转型的日益重视也推动了对 MLOps 解决方案的需求。

免费客製化服务：

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场公司的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争标基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第 5 章：全球 MLOps 市场：按组成部分

  • 平台
  • 服务

第 6 章 MLOps 的全球市场：依部署分类

  • 云
  • 本地
  • 混合

第 7 章：全球 MLOps 市场：依公司类型

  • 小型企业
  • 大公司

第八章全球 MLOps 市场：依应用分类

  • 资料管理
  • 基础设施模型
  • 其他的

第 9 章：全球 MLOps 市场：依最终使用者分类

  • 资讯科技/通讯
  • 医疗保健/生命科学
  • 银行、金融服务和保险
  • 製造业
  • 零售
  • 政府和公共部门
  • 广告
  • 运输/物流
  • 能源和公共
  • 其他的

第 10 章 全球 MLOps 市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东/非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东/非洲

第十一章 主要进展

  • 合约、伙伴关係、合作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章 公司概况

  • 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.