全球 MLOps 市場規模、佔有率、成長分析，依基礎設施（資料基礎設施和模型基礎設施）、資料管理（資料管道管理和資料版本控制）分類 - 2024-2031 年產業預測

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個工作天內

價格

簡介目錄

2022 年，全球 MLOps 市場規模為 11 億美元，預計將從 2023 年的 15.5 億美元成長到 2031 年的 242.3 億美元，在預測期內（2024-2031 年）複合年成長率為 41%。

MLOps 是一種促進機器學習模式大規模部署的技術，由於其解決人為錯誤和品質問題的能力，醫療保健、IT 和零售等各個行業的採用率激增。全球數位和網路普及率的不斷提高推動了這種採用趨勢，為 MLOps 市場帶來了利潤豐厚的成長前景。此外，企業擴大採用 MLOps 來提高營運和生產力，進一步促進市場成長。值得注意的是，預計醫療保健行業的投資將在預測期內為市場擴張做出重大貢獻。儘管前景廣闊，但資料無法存取、資料安全問題、僵化的商業模式以及工程技能短缺等課題對 MLOps 市場的成長構成了障礙。儘管如此，MLOps 簡化機器學習生命週期和降低成本的能力使其在未來幾年能夠大幅成長。

市場概況
北美洲
- 美國
- 加拿大
歐洲
- 德國
- 西班牙
- 法國
- 英國
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 韓國
- 亞太其他地區
拉丁美洲
- 巴西
- 拉丁美洲其他地區
中東和非洲 (MEA)
- 海灣合作理事會國家
- 南非
- MEA 的其餘部分

競爭格局

前 5 名企業比較
2021 年關鍵參與者的市場定位
主要市場參與者所採取的策略
最佳制勝策略
近期市集活動
主要公司市佔率（%），2021年

主要公司簡介

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)

簡介目錄

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