全球 MLOps 市场规模、份额、成长分析，按基础设施（资料基础设施和模型基础设施）、资料管理（资料管道管理和资料版本控制）划分 - 2024-2031 年产业预测

Global MLOps Market Size, Share, Growth Analysis, By Infrastructure(Data Infrastructure and Model Infrastructure), By Data Management(Data Pipeline Management and Data Versioning) - Industry Forecast 2024-2031

出版日期: 2024年04月09日 | 出版商:

SkyQuest | 英文 197 Pages | 商品交期: 3-5个工作天内

价格

简介目录

2022 年，全球 MLOps 市场规模为 11 亿美元，预计将从 2023 年的 15.5 亿美元成长到 2031 年的 242.3 亿美元，在预测期内（2024-2031 年）复合年增长率为 41%。

MLOps 是一种促进机器学习模式大规模部署的技术，由于其解决人为错误和品质问题的能力，医疗保健、IT 和零售等各个行业的采用率激增。全球数位和网路普及率的不断提高推动了这种采用趋势，为 MLOps 市场带来了利润丰厚的成长前景。此外，企业越来越多地采用 MLOps 来提高营运和生产力，进一步促进市场成长。值得注意的是，预计医疗保健行业的投资将在预测期内为市场扩张做出重大贡献。儘管前景广阔，但资料无法存取、资料安全问题、僵化的商业模式以及工程技能短缺等挑战对 MLOps 市场的成长构成了障碍。儘管如此，MLOps 简化机器学习生命週期和降低成本的能力使其在未来几年能够大幅成长。

市场概况
北美洲
- 美国
- 加拿大
欧洲
- 德国
- 西班牙
- 法国
- 英国
- 欧洲其他地区
亚太地区
- 中国
- 印度
- 日本
- 韩国
- 亚太其他地区
拉丁美洲
- 巴西
- 拉丁美洲其他地区
中东和非洲 (MEA)
- 海湾合作委员会国家
- 南非
- MEA 的其余部分

竞争格局

前 5 名玩家比较
2021 年关键参与者的市场定位
主要市场参与者所采取的策略
最佳制胜策略
近期市集活动
主要公司市占率（%），2021年

主要公司简介

IBM (US)
Microsoft (US)
Google (US)
AWS (US)
HE (US)
GAVS Technologies (US)
DataRobot (US)
Cloudera (US)
Altery (US)
Domino Data Lab (US)
Valohai (US)
H20.ai (US)
MLflow (Netherlands)
Neptune. ai (Europe)
Comet (US)
SparkCognition (US)
Hopsworks (Europe)
Datatron (US)
Weights & Biases (US)
Katonic.ai (Australia)

简介目录

Product Code: SQMIG45D2064

Global MLOps Market size was valued at USD 1.10 billion in 2022 and is poised to grow from USD 1.55 billion in 2023 to USD 24.23 billion by 2031, growing at a CAGR of 41% during the forecast period (2024-2031).

MLOps, the technology facilitating the deployment of machine learning models at scale, is witnessing a surge in adoption across various sectors like healthcare, IT, and retail due to its capacity to address human errors and quality issues. This adoption trend is fueled by the increasing digital and internet penetration worldwide, presenting lucrative growth prospects for the MLOps market. Furthermore, enterprises are increasingly embracing MLOps to enhance their operations and productivity, further bolstering market growth. Notably, investments in the healthcare sector are anticipated to contribute significantly to market expansion in the forecast period. Despite the promising outlook, challenges such as inaccessible data, data security concerns, rigid business models, and a shortage of engineering skills pose obstacles to the growth of the MLOps market. Nevertheless, the ability of MLOps to streamline the machine learning lifecycle and reduce costs positions it for substantial growth in the coming years.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global MLOps Market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global MLOps Market Segmental Analysis

The global MLOps market is segmented by infrastructure, data management, and region. Based on infrastructure, the market can be segmented into data infrastructure and model infrastructure. Based on data management the market is segmented into data pipeline management and data versioning. Based on region, the market is segmented into North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.

Drivers of the Global MLOps Market

Manual data reprocessing and collection often yield unsatisfactory results, highlighting the inefficiency of traditional methods. MLOps offers a comprehensive solution by automating the entire machine learning model workflow, encompassing data gathering, model development, testing, retraining, and deployment. By implementing MLOps practices, companies can significantly save time and reduce error rates. Moreover, it fosters collaboration among IT and business personnel, data scientists, and engineers, facilitating the widespread adoption of machine learning models across the organization. With MLOps principles, businesses can standardize their ML operations and establish a unified language for all stakeholders, mitigating compatibility issues and accelerating the construction and deployment of modeling processes.

Restraints in the Global MLOps Market

Organizations are increasingly recognizing the importance of utilizing MLOps in data management to effectively gather and integrate vast volumes of data from various sources and break down data silos. However, the adoption of MLOps models is hindered by knowledge gaps and a lack of skilled workers within these organizations. As businesses often operate in silos, the need for MLOps becomes more critical to gain insights across multiple applications and verticals. Surveys consistently highlight the insufficient knowledge and capabilities of employees in handling MLOps, underscoring the urgency for investment in training and certifications. It is imperative for organizations to prioritize equipping their workforce with the necessary understanding and skills in MLOps models and strategies to ensure effective data management practices are implemented.

Market Trends of the Global MLOps Market

In the global MLOps market, significant trends include the integration of more automation features within MLOps platforms, such as auto-model selection and tuning, enabling businesses to streamline model development processes and cut down on time and costs. Additionally, there's a notable shift towards cloud-based MLOps platforms due to their scalability and flexibility, with cloud providers expanding their offerings to include services like automatic model deployment and monitoring. Effective MLOps relies on collaboration between data scientists and IT operations teams, prompting platforms to integrate collaboration features. Furthermore, as machine learning models grow in complexity, the trend of Explainable AI emerges, aiming to provide insights into the decision-making process of these models. Given the increasing use of sensitive data, security remains a top priority, leading MLOps platforms to incorporate robust security features to safeguard data and mitigate cyber threats.

Executive Summary

Market Overview
Wheel of Fortune

Research Methodology

Information Procurement
Secondary & Primary Data Sources
Market Size Estimation
Market Assumptions & Limitations

Parent Market Analysis

Market Overview
Market Size
Market Dynamics
- Drivers
- Opportunities
- Restraints
- Challenges

Key Market Insights

Technology Analysis
Pricing Analysis
Supply Chain Analysis
Value Chain Analysis
Ecosystem of the Market
IP Analysis
Trade Analysis
Startup Analysis
Raw Material Analysis
Innovation Matrix
Pipeline Product Analysis
Macroeconomic Indicators
Top Investment Analysis
Key Success Factor
Degree of Competition

Market Dynamics & Outlook

Market Dynamics
- Drivers
- Opportunities
- Restraints
- Challenges
Regulatory Landscape
Porters Analysis
- Competitive rivalry
- Threat of Substitute Products
- Bargaining Power of Buyers
- Threat of New Entrants
- Bargaining Power of Suppliers
Skyquest Special Insights on Future Disruptions
- Political Impact
- Economic Impact
- Social Impact
- Technical Impact
- Environmental Impact
- Legal Impact

Global MLOps Market by Infrastructure

Market Overview
Data Infrastructure and Model Infrastructure

Global MLOps Market by Data Management

Market Overview
Data Pipeline Management and Data Versioning

Global MLOps Market Size by Region

Market Overview
North America
- USA
- Canada
Europe
- Germany
- Spain
- France
- UK
- Rest of Europe
Asia Pacific
- China
- India
- Japan
- South Korea
- Rest of Asia-Pacific
Latin America
- Brazil
- Rest of Latin America
Middle East & Africa (MEA)
- GCC Countries
- South Africa
- Rest of MEA

Competitive Landscape

Top 5 Player Comparison
Market Positioning of Key Players, 2021
Strategies Adopted by Key Market Players
Top Winning Strategies
- By Development
- By Company
- By Year
Recent Activities in the Market
Key Companies Market Share (%), 2021

Key Company Profiles

IBM (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Microsoft (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Google (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
AWS (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
HE (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
GAVS Technologies (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
DataRobot (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Cloudera (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Altery (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Domino Data Lab (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Valohai (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
H20.ai (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
MLflow (Netherlands)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Neptune. ai (Europe)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Comet (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
SparkCognition (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Hopsworks (Europe)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Datatron (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Weights & Biases (US)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments
Katonic.ai (Australia)
- Company Overview
- Business Segment Overview
- Financial Updates
- Key Developments