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市场调查报告书
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1868022

机器学习维运（MLOps）：2025-2031年全球市占率及排名、总收入及需求预测

Machine Learning Operations (MLOps) - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031
出版日期: | 出版商: QYResearch | 英文 126 Pages | 商品交期: 2-3个工作天内

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全球机器学习运维（MLOps）市场规模预计在 2024 年达到 19.76 亿美元，预计到 2031 年将达到 225.17 亿美元，2025 年至 2031 年的复合年增长率为 38.3%。

机器学习运作（MLOps）是指一套将机器学习模型的开发和运作紧密结合的实践、工具和流程。它旨在将传统的软体开发DevOps概念引入机器学习领域，消除资料科学家、工程师和维运团队之间的协作障碍。这使得机器学习的整个生命週期得以自动化和高效管理，涵盖资料准备、模型训练、模型评估、模型配置以及模型监控和维护等各个环节。透过MLOps，企业可以加速机器学习模型从实验阶段到生产阶段的过渡，确保模型在生产环境中稳定运作，并持续优化，最终为企业创造显着价值。

MLOps市场目前正经历快速发展。随着全球各产业数位转型加速以及人工智慧和机器学习技术的应用日益广泛，MLOps的重要性日益凸显。

该市场具有以下特点：

应用范围广泛：在金融领域，银行和保险公司利用它来优化风险评估模型并提高欺诈检测效率；在医疗领域，它用于支持疾病预测和医学影像诊断；在零售领域，它用于精准营销和优化库存管理；在製造业，它用于加强品管和预测设备故障。各行业对MLOps的积极探索和应用正在推动市场规模的持续扩大。

竞争格局正逐渐形成：AWS、Google Cloud 和 Microsoft Azure 等主要云端运算供应商正凭藉其强大的云端基础架构和丰富的 AI 服务生态系统进军机器学习维运 (MLOps) 领域。 DataRobot 和 H2O.ai 等机器学习平台专家在 MLOps 解决方案方面拥有深厚的技术专长。同时，越来越多Start-Ups创公司涌现，凭藉着创新技术和独特的服务模式在细分市场中脱颖而出。整体竞争格局日趋多元化，各公司透过产品创新、策略合作、併购争取市场占有率。

多元化的需求因素：一方面，企业迫切需要提升机器学习计划开发效率，缩短模型部署时间。传统的机器学习计划常面临开发週期长、模型部署困难、维修成本高等挑战。 MLOps 提供自动化流程和标准化工具，有效解决这些难题。另一方面，随着资料量的爆炸性成长和模型复杂性的不断提高，企业需要更专业的技术手段来管理模型的整个生命週期，确保模型效能的可靠性和稳定性。此外，跨部门协作的需求也推动了 MLOps 的应用，打破了资料科学团队和 IT 维运团队之间的沟通壁垒，实现了高效协作。

趋势

与云端原生技术的深度整合：展望未来，MLOps 将与云端原生技术更加紧密地整合。云端容器化架构（例如 Docker 和 Kubernetes 等容器编排管理工具）为 MLOps 提供高效的资源管理、灵活的部署方式和强大的可扩充性。透过利用云端原生技术，企业可以轻鬆地在不同的云端和混合云端环境中快速部署和迁移机器学习模型，从而显着降低基础设施管理成本，同时提高整个系统的容错性和可靠性。

持续推进自动化：自动化是机器学习运作维护（MLOps）的核心发展方向之一。从资料收集、清洗、标註到模型训练、调优、评估，甚至模型部署和监控，每个环节都将高度自动化。例如，自动化机器学习（AutoML）技术将进一步发展，能够自动选择最优演算法、参数设定和资料预处理方法，显着减少人工干预，提高机器学习计划的开发效率。同时，系统将透过事件驱动的自动化流程即时监控模型效能。如果模型效能偏离预期或资料分布发生变化，系统将自动触发模型重新训练或调整，确保模型始终保持最佳效能。

专注于模型可解释性和合规性：随着机器学习模型在金融、医疗、法律等关键业务领域的广泛应用，模型可解释性和合规性已成为重要关注点。未来的MLOps平台将整合更多可解释性工具，帮助使用者理解模型的决策流程和输出结果，进而增强使用者对模型的信任。此外，从资料隐私保护和法规遵循的角度来看，MLOps将提供更全面的解决方案，帮助企业在严格遵守相关法规（例如欧盟《一般资料保护规则》 (GDPR)）的同时，有效利用机器学习技术。

边缘机器学习运维的崛起：随着物联网设备的激增以及对即时数据分析和处理需求的日益增长，边缘运算在机器学习领域备受关注。边缘机器学习运维旨在将机器学习模型的部署和运行从云端扩展到边缘设备，从而实现快速的本地资料处理和决策。这不仅可以降低资料传输延迟和网路频宽消耗，还能增强资料安全性和隐私性。展望未来，边缘机器学习维运将成为机器学习维运市场的关键成长领域，相关技术和产品也将持续涌现，以满足各产业在边缘场景下机器学习的多样化应用需求。

本报告旨在按地区/国家、类型和应用对全球机器学习运维（MLOps）市场进行全面分析，重点关注总收入、市场份额和主要企业的排名。

机器学习维运（MLOps）市场规模、估算和预测以销售收入为指标，以 2024 年为基准年，并包含 2020 年至 2031 年的历史数据和预测数据。报告采用定量和定性分析相结合的方法，帮助读者制定业务/成长策略，评估市场竞争格局，分析公司在当前市场中的地位，并就机器学习运维（MLOps）做出明智的业务决策。

市场区隔

公司

  • IBM
  • DataRobot
  • SAS
  • Microsoft
  • Amazon
  • Google
  • Dataiku
  • Databricks
  • HPE
  • Lguazio
  • ClearML
  • Modzy
  • Comet
  • Cloudera
  • Paperpace
  • Valohai

按类型分類的细分市场

  • 本地部署
  • 云
  • 其他的

应用领域

  • BFSI
  • 医疗保健
  • 零售
  • 製造业
  • 公共部门
  • 其他的

按地区

  • 北美洲
    • 美国
    • 加拿大
  • 亚太地区
    • 中国
    • 日本
    • 韩国
    • 东南亚
    • 印度
    • 澳洲
    • 亚太其他地区
  • 欧洲
    • 德国
    • 法国
    • 英国
    • 义大利
    • 荷兰
    • 北欧国家
    • 其他欧洲
  • 拉丁美洲
    • 墨西哥
    • 巴西
    • 其他拉丁美洲
  • 中东和非洲
    • 土耳其
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 其他中东和非洲地区

The global market for Machine Learning Operations (MLOps) was estimated to be worth US$ 1976 million in 2024 and is forecast to a readjusted size of US$ 22517 million by 2031 with a CAGR of 38.3% during the forecast period 2025-2031.

Machine Learning Operations (MLOps) is a set of practices, tools, and processes that tightly integrate machine learning model development and operations. It introduces the DevOps philosophy from traditional software development into the machine learning domain, aiming to break down collaboration barriers between data scientists, engineers, and operations teams. This enables the automation and efficient management of the entire machine learning lifecycle, from data preparation, model training, model evaluation, model deployment, to model monitoring and maintenance. Through MLOps, businesses can accelerate the transition of machine learning models from the experimental stage to production environments, ensuring that models operate stably and are continuously optimized in real-world applications, ultimately creating greater value for the business.

Currently, the MLOps market is undergoing rapid development. With the acceleration of digital transformation across industries worldwide and the increasing application of artificial intelligence and machine learning technologies, the importance of MLOps is becoming increasingly evident.

The market exhibits the following characteristics:

Wide-ranging application areas: In the financial sector, MLOps helps banks and insurance companies optimize risk assessment models and improve fraud detection efficiency; in the healthcare industry, MLOps enables disease prediction and assists in medical imaging diagnosis; in the retail sector, MLOps is used for precision marketing and inventory management optimization; and in manufacturing, MLOps is employed to enhance quality control and predict equipment failures. The active exploration and application of MLOps across industries are driving the continuous expansion of the market size.

Competitive landscape gradually taking shape: In the market, large cloud computing providers such as AWS, Google Cloud, and Microsoft Azure are entering the MLOps field leveraging their robust cloud infrastructure and rich AI service ecosystems; companies specializing in machine learning platforms, such as DataRobot and H2O.ai, possess deep technical expertise in MLOps solutions; simultaneously, emerging startups are continuously emerging, distinguishing themselves in niche markets through innovative technologies and unique service models. The overall competitive landscape is becoming increasingly diversified, with companies vying for market share through product innovation, strategic partnerships, and mergers and acquisitions.

Diverse demand drivers: On one hand, businesses have an urgent need to improve the efficiency of machine learning project development and reduce the time required to deploy models. Traditional machine learning projects often face challenges such as lengthy development cycles, difficulties in model deployment, and high maintenance costs. MLOps provides automated processes and standardized tools that can effectively address these pain points. On the other hand, with the explosive growth of data volume and the increasing complexity of models, companies need more specialized technical means to manage the entire model lifecycle and ensure the reliability and stability of model performance. Additionally, the need for cross-departmental collaboration has prompted companies to adopt MLOps to break down communication barriers between data science teams and IT operations teams, enabling efficient collaboration.

Trends

Deep integration with cloud-native technologies: In the future, MLOps will become more closely integrated with cloud-native technologies. Cloud-native architectures (such as containerization technology Docker and container orchestration tools like Kubernetes) provide MLOps with efficient resource management, flexible deployment methods, and robust scalability. By leveraging cloud-native technologies, enterprises can easily achieve rapid deployment and migration of machine learning models across different cloud environments or hybrid cloud environments, significantly reducing infrastructure management costs while enhancing the overall resilience and reliability of the system.

Continuously improving automation: Automation is one of the core development directions of MLOps. From data collection, cleaning, and labeling, to model training, tuning, and evaluation, to model deployment and monitoring, each link will achieve a higher degree of automation. For example, automated machine learning (AutoML) technology will further develop, enabling the automatic selection of the optimal algorithms, parameter configurations, and data preprocessing methods, greatly reducing manual intervention and improving the development efficiency of machine learning projects. At the same time, event-driven automated processes will monitor model performance in real time. When model performance deviates from expectations or data distribution changes, the system will automatically trigger model retraining or adjustments to ensure the model maintains optimal performance.

Emphasis on model explainability and compliance: As machine learning models are widely adopted in critical business domains such as finance, healthcare, and law, model explainability and compliance have become key concerns. Future MLOps platforms will integrate more explainability tools to help users understand the decision-making process and output results of models, thereby enhancing trust in the models. Additionally, in terms of data privacy protection and regulatory compliance, MLOps will provide more comprehensive solutions to ensure that enterprises strictly adhere to relevant laws and regulations when using machine learning technologies, such as the European Union's General Data Protection Regulation (GDPR).

The Rise of Edge MLOps: With the widespread adoption of IoT devices and increasing demand for real-time data analysis and processing, edge computing is gaining increasing attention in the field of machine learning. Edge MLOps aims to extend the deployment and operation of machine learning models from the cloud to edge devices, enabling rapid local data processing and decision-making. This not only reduces data transmission latency and network bandwidth consumption but also enhances data security and privacy. In the future, edge MLOps will become an important growth area in the MLOps market, with related technologies and products continuously emerging to meet the diverse application needs of machine learning in edge scenarios across various industries.

This report aims to provide a comprehensive presentation of the global market for Machine Learning Operations (MLOps), focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Machine Learning Operations (MLOps) by region & country, by Type, and by Application.

The Machine Learning Operations (MLOps) market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning Operations (MLOps).

Market Segmentation

By Company

  • IBM
  • DataRobot
  • SAS
  • Microsoft
  • Amazon
  • Google
  • Dataiku
  • Databricks
  • HPE
  • Lguazio
  • ClearML
  • Modzy
  • Comet
  • Cloudera
  • Paperpace
  • Valohai

Segment by Type

  • On-premise
  • Cloud
  • Others

Segment by Application

  • BFSI
  • Healthcare
  • Retail
  • Manufacturing
  • Public Sector
  • Others

By Region

  • North America
    • United States
    • Canada
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • Southeast Asia
    • India
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • U.K.
    • Italy
    • Netherlands
    • Nordic Countries
    • Rest of Europe
  • Latin America
    • Mexico
    • Brazil
    • Rest of Latin America
  • Middle East & Africa
    • Turkey
    • Saudi Arabia
    • UAE
    • Rest of MEA

Chapter Outline

Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.

Chapter 2: Detailed analysis of Machine Learning Operations (MLOps) company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.

Chapter 5: Revenue of Machine Learning Operations (MLOps) in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 6: Revenue of Machine Learning Operations (MLOps) in country level. It provides sigmate data by Type, and by Application for each country/region.

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.

Table of Contents

1 Market Overview

  • 1.1 Machine Learning Operations (MLOps) Product Introduction
  • 1.2 Global Machine Learning Operations (MLOps) Market Size Forecast (2020-2031)
  • 1.3 Machine Learning Operations (MLOps) Market Trends & Drivers
    • 1.3.1 Machine Learning Operations (MLOps) Industry Trends
    • 1.3.2 Machine Learning Operations (MLOps) Market Drivers & Opportunity
    • 1.3.3 Machine Learning Operations (MLOps) Market Challenges
    • 1.3.4 Machine Learning Operations (MLOps) Market Restraints
  • 1.4 Assumptions and Limitations
  • 1.5 Study Objectives
  • 1.6 Years Considered

2 Competitive Analysis by Company

  • 2.1 Global Machine Learning Operations (MLOps) Players Revenue Ranking (2024)
  • 2.2 Global Machine Learning Operations (MLOps) Revenue by Company (2020-2025)
  • 2.3 Key Companies Machine Learning Operations (MLOps) Manufacturing Base Distribution and Headquarters
  • 2.4 Key Companies Machine Learning Operations (MLOps) Product Offered
  • 2.5 Key Companies Time to Begin Mass Production of Machine Learning Operations (MLOps)
  • 2.6 Machine Learning Operations (MLOps) Market Competitive Analysis
    • 2.6.1 Machine Learning Operations (MLOps) Market Concentration Rate (2020-2025)
    • 2.6.2 Global 5 and 10 Largest Companies by Machine Learning Operations (MLOps) Revenue in 2024
    • 2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning Operations (MLOps) as of 2024)
  • 2.7 Mergers & Acquisitions, Expansion

3 Segmentation by Type

  • 3.1 Introduction by Type
    • 3.1.1 On-premise
    • 3.1.2 Cloud
    • 3.1.3 Others
  • 3.2 Global Machine Learning Operations (MLOps) Sales Value by Type
    • 3.2.1 Global Machine Learning Operations (MLOps) Sales Value by Type (2020 VS 2024 VS 2031)
    • 3.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Type (2020-2031)
    • 3.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Type (%) (2020-2031)

4 Segmentation by Application

  • 4.1 Introduction by Application
    • 4.1.1 BFSI
    • 4.1.2 Healthcare
    • 4.1.3 Retail
    • 4.1.4 Manufacturing
    • 4.1.5 Public Sector
    • 4.1.6 Others
  • 4.2 Global Machine Learning Operations (MLOps) Sales Value by Application
    • 4.2.1 Global Machine Learning Operations (MLOps) Sales Value by Application (2020 VS 2024 VS 2031)
    • 4.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Application (2020-2031)
    • 4.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Application (%) (2020-2031)

5 Segmentation by Region

  • 5.1 Global Machine Learning Operations (MLOps) Sales Value by Region
    • 5.1.1 Global Machine Learning Operations (MLOps) Sales Value by Region: 2020 VS 2024 VS 2031
    • 5.1.2 Global Machine Learning Operations (MLOps) Sales Value by Region (2020-2025)
    • 5.1.3 Global Machine Learning Operations (MLOps) Sales Value by Region (2026-2031)
    • 5.1.4 Global Machine Learning Operations (MLOps) Sales Value by Region (%), (2020-2031)
  • 5.2 North America
    • 5.2.1 North America Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 5.2.2 North America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • 5.3 Europe
    • 5.3.1 Europe Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 5.3.2 Europe Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • 5.4 Asia Pacific
    • 5.4.1 Asia Pacific Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 5.4.2 Asia Pacific Machine Learning Operations (MLOps) Sales Value by Region (%), 2024 VS 2031
  • 5.5 South America
    • 5.5.1 South America Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 5.5.2 South America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • 5.6 Middle East & Africa
    • 5.6.1 Middle East & Africa Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 5.6.2 Middle East & Africa Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031

6 Segmentation by Key Countries/Regions

  • 6.1 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value Growth Trends, 2020 VS 2024 VS 2031
  • 6.2 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value, 2020-2031
  • 6.3 United States
    • 6.3.1 United States Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.3.2 United States Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.3.3 United States Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.4 Europe
    • 6.4.1 Europe Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.4.2 Europe Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.4.3 Europe Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.5 China
    • 6.5.1 China Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.5.2 China Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.5.3 China Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.6 Japan
    • 6.6.1 Japan Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.6.2 Japan Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.6.3 Japan Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.7 South Korea
    • 6.7.1 South Korea Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.7.2 South Korea Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.7.3 South Korea Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.8 Southeast Asia
    • 6.8.1 Southeast Asia Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.8.2 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.8.3 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
  • 6.9 India
    • 6.9.1 India Machine Learning Operations (MLOps) Sales Value, 2020-2031
    • 6.9.2 India Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
    • 6.9.3 India Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031

7 Company Profiles

  • 7.1 IBM
    • 7.1.1 IBM Profile
    • 7.1.2 IBM Main Business
    • 7.1.3 IBM Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.1.4 IBM Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.1.5 IBM Recent Developments
  • 7.2 DataRobot
    • 7.2.1 DataRobot Profile
    • 7.2.2 DataRobot Main Business
    • 7.2.3 DataRobot Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.2.4 DataRobot Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.2.5 DataRobot Recent Developments
  • 7.3 SAS
    • 7.3.1 SAS Profile
    • 7.3.2 SAS Main Business
    • 7.3.3 SAS Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.3.4 SAS Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.3.5 SAS Recent Developments
  • 7.4 Microsoft
    • 7.4.1 Microsoft Profile
    • 7.4.2 Microsoft Main Business
    • 7.4.3 Microsoft Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.4.4 Microsoft Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.4.5 Microsoft Recent Developments
  • 7.5 Amazon
    • 7.5.1 Amazon Profile
    • 7.5.2 Amazon Main Business
    • 7.5.3 Amazon Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.5.4 Amazon Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.5.5 Amazon Recent Developments
  • 7.6 Google
    • 7.6.1 Google Profile
    • 7.6.2 Google Main Business
    • 7.6.3 Google Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.6.4 Google Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.6.5 Google Recent Developments
  • 7.7 Dataiku
    • 7.7.1 Dataiku Profile
    • 7.7.2 Dataiku Main Business
    • 7.7.3 Dataiku Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.7.4 Dataiku Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.7.5 Dataiku Recent Developments
  • 7.8 Databricks
    • 7.8.1 Databricks Profile
    • 7.8.2 Databricks Main Business
    • 7.8.3 Databricks Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.8.4 Databricks Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.8.5 Databricks Recent Developments
  • 7.9 HPE
    • 7.9.1 HPE Profile
    • 7.9.2 HPE Main Business
    • 7.9.3 HPE Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.9.4 HPE Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.9.5 HPE Recent Developments
  • 7.10 Lguazio
    • 7.10.1 Lguazio Profile
    • 7.10.2 Lguazio Main Business
    • 7.10.3 Lguazio Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.10.4 Lguazio Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.10.5 Lguazio Recent Developments
  • 7.11 ClearML
    • 7.11.1 ClearML Profile
    • 7.11.2 ClearML Main Business
    • 7.11.3 ClearML Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.11.4 ClearML Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.11.5 ClearML Recent Developments
  • 7.12 Modzy
    • 7.12.1 Modzy Profile
    • 7.12.2 Modzy Main Business
    • 7.12.3 Modzy Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.12.4 Modzy Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.12.5 Modzy Recent Developments
  • 7.13 Comet
    • 7.13.1 Comet Profile
    • 7.13.2 Comet Main Business
    • 7.13.3 Comet Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.13.4 Comet Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.13.5 Comet Recent Developments
  • 7.14 Cloudera
    • 7.14.1 Cloudera Profile
    • 7.14.2 Cloudera Main Business
    • 7.14.3 Cloudera Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.14.4 Cloudera Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.14.5 Cloudera Recent Developments
  • 7.15 Paperpace
    • 7.15.1 Paperpace Profile
    • 7.15.2 Paperpace Main Business
    • 7.15.3 Paperpace Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.15.4 Paperpace Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.15.5 Paperpace Recent Developments
  • 7.16 Valohai
    • 7.16.1 Valohai Profile
    • 7.16.2 Valohai Main Business
    • 7.16.3 Valohai Machine Learning Operations (MLOps) Products, Services and Solutions
    • 7.16.4 Valohai Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
    • 7.16.5 Valohai Recent Developments

8 Industry Chain Analysis

  • 8.1 Machine Learning Operations (MLOps) Industrial Chain
  • 8.2 Machine Learning Operations (MLOps) Upstream Analysis
    • 8.2.1 Key Raw Materials
    • 8.2.2 Raw Materials Key Suppliers
    • 8.2.3 Manufacturing Cost Structure
  • 8.3 Midstream Analysis
  • 8.4 Downstream Analysis (Customers Analysis)
  • 8.5 Sales Model and Sales Channels
    • 8.5.1 Machine Learning Operations (MLOps) Sales Model
    • 8.5.2 Sales Channel
    • 8.5.3 Machine Learning Operations (MLOps) Distributors

9 Research Findings and Conclusion

10 Appendix

  • 10.1 Research Methodology
    • 10.1.1 Methodology/Research Approach
      • 10.1.1.1 Research Programs/Design
      • 10.1.1.2 Market Size Estimation
      • 10.1.1.3 Market Breakdown and Data Triangulation
    • 10.1.2 Data Source
      • 10.1.2.1 Secondary Sources
      • 10.1.2.2 Primary Sources
  • 10.2 Author Details
  • 10.3 Disclaimer

List of Tables

  • Table 1. Machine Learning Operations (MLOps) Market Trends
  • Table 2. Machine Learning Operations (MLOps) Market Drivers & Opportunity
  • Table 3. Machine Learning Operations (MLOps) Market Challenges
  • Table 4. Machine Learning Operations (MLOps) Market Restraints
  • Table 5. Global Machine Learning Operations (MLOps) Revenue by Company (2020-2025) & (US$ Million)
  • Table 6. Global Machine Learning Operations (MLOps) Revenue Market Share by Company (2020-2025)
  • Table 7. Key Companies Machine Learning Operations (MLOps) Manufacturing Base Distribution and Headquarters
  • Table 8. Key Companies Machine Learning Operations (MLOps) Product Type
  • Table 9. Key Companies Time to Begin Mass Production of Machine Learning Operations (MLOps)
  • Table 10. Global Machine Learning Operations (MLOps) Companies Market Concentration Ratio (CR5 and HHI)
  • Table 11. Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning Operations (MLOps) as of 2024)
  • Table 12. Mergers & Acquisitions, Expansion Plans
  • Table 13. Global Machine Learning Operations (MLOps) Sales Value by Type: 2020 VS 2024 VS 2031 (US$ Million)
  • Table 14. Global Machine Learning Operations (MLOps) Sales Value by Type (2020-2025) & (US$ Million)
  • Table 15. Global Machine Learning Operations (MLOps) Sales Value by Type (2026-2031) & (US$ Million)
  • Table 16. Global Machine Learning Operations (MLOps) Sales Market Share in Value by Type (2020-2025)
  • Table 17. Global Machine Learning Operations (MLOps) Sales Market Share in Value by Type (2026-2031)
  • Table 18. Global Machine Learning Operations (MLOps) Sales Value by Application: 2020 VS 2024 VS 2031 (US$ Million)
  • Table 19. Global Machine Learning Operations (MLOps) Sales Value by Application (2020-2025) & (US$ Million)
  • Table 20. Global Machine Learning Operations (MLOps) Sales Value by Application (2026-2031) & (US$ Million)
  • Table 21. Global Machine Learning Operations (MLOps) Sales Market Share in Value by Application (2020-2025)
  • Table 22. Global Machine Learning Operations (MLOps) Sales Market Share in Value by Application (2026-2031)
  • Table 23. Global Machine Learning Operations (MLOps) Sales Value by Region, (2020 VS 2024 VS 2031) & (US$ Million)
  • Table 24. Global Machine Learning Operations (MLOps) Sales Value by Region (2020-2025) & (US$ Million)
  • Table 25. Global Machine Learning Operations (MLOps) Sales Value by Region (2026-2031) & (US$ Million)
  • Table 26. Global Machine Learning Operations (MLOps) Sales Value by Region (2020-2025) & (%)
  • Table 27. Global Machine Learning Operations (MLOps) Sales Value by Region (2026-2031) & (%)
  • Table 28. Key Countries/Regions Machine Learning Operations (MLOps) Sales Value Growth Trends, (US$ Million): 2020 VS 2024 VS 2031
  • Table 29. Key Countries/Regions Machine Learning Operations (MLOps) Sales Value, (2020-2025) & (US$ Million)
  • Table 30. Key Countries/Regions Machine Learning Operations (MLOps) Sales Value, (2026-2031) & (US$ Million)
  • Table 31. IBM Basic Information List
  • Table 32. IBM Description and Business Overview
  • Table 33. IBM Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 34. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of IBM (2020-2025)
  • Table 35. IBM Recent Developments
  • Table 36. DataRobot Basic Information List
  • Table 37. DataRobot Description and Business Overview
  • Table 38. DataRobot Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 39. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of DataRobot (2020-2025)
  • Table 40. DataRobot Recent Developments
  • Table 41. SAS Basic Information List
  • Table 42. SAS Description and Business Overview
  • Table 43. SAS Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 44. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of SAS (2020-2025)
  • Table 45. SAS Recent Developments
  • Table 46. Microsoft Basic Information List
  • Table 47. Microsoft Description and Business Overview
  • Table 48. Microsoft Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 49. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Microsoft (2020-2025)
  • Table 50. Microsoft Recent Developments
  • Table 51. Amazon Basic Information List
  • Table 52. Amazon Description and Business Overview
  • Table 53. Amazon Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 54. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Amazon (2020-2025)
  • Table 55. Amazon Recent Developments
  • Table 56. Google Basic Information List
  • Table 57. Google Description and Business Overview
  • Table 58. Google Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 59. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Google (2020-2025)
  • Table 60. Google Recent Developments
  • Table 61. Dataiku Basic Information List
  • Table 62. Dataiku Description and Business Overview
  • Table 63. Dataiku Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 64. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Dataiku (2020-2025)
  • Table 65. Dataiku Recent Developments
  • Table 66. Databricks Basic Information List
  • Table 67. Databricks Description and Business Overview
  • Table 68. Databricks Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 69. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Databricks (2020-2025)
  • Table 70. Databricks Recent Developments
  • Table 71. HPE Basic Information List
  • Table 72. HPE Description and Business Overview
  • Table 73. HPE Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 74. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of HPE (2020-2025)
  • Table 75. HPE Recent Developments
  • Table 76. Lguazio Basic Information List
  • Table 77. Lguazio Description and Business Overview
  • Table 78. Lguazio Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 79. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Lguazio (2020-2025)
  • Table 80. Lguazio Recent Developments
  • Table 81. ClearML Basic Information List
  • Table 82. ClearML Description and Business Overview
  • Table 83. ClearML Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 84. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of ClearML (2020-2025)
  • Table 85. ClearML Recent Developments
  • Table 86. Modzy Basic Information List
  • Table 87. Modzy Description and Business Overview
  • Table 88. Modzy Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 89. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Modzy (2020-2025)
  • Table 90. Modzy Recent Developments
  • Table 91. Comet Basic Information List
  • Table 92. Comet Description and Business Overview
  • Table 93. Comet Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 94. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Comet (2020-2025)
  • Table 95. Comet Recent Developments
  • Table 96. Cloudera Basic Information List
  • Table 97. Cloudera Description and Business Overview
  • Table 98. Cloudera Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 99. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Cloudera (2020-2025)
  • Table 100. Cloudera Recent Developments
  • Table 101. Paperpace Basic Information List
  • Table 102. Paperpace Description and Business Overview
  • Table 103. Paperpace Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 104. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Paperpace (2020-2025)
  • Table 105. Paperpace Recent Developments
  • Table 106. Valohai Basic Information List
  • Table 107. Valohai Description and Business Overview
  • Table 108. Valohai Machine Learning Operations (MLOps) Products, Services and Solutions
  • Table 109. Revenue (US$ Million) in Machine Learning Operations (MLOps) Business of Valohai (2020-2025)
  • Table 110. Valohai Recent Developments
  • Table 111. Key Raw Materials Lists
  • Table 112. Raw Materials Key Suppliers Lists
  • Table 113. Machine Learning Operations (MLOps) Downstream Customers
  • Table 114. Machine Learning Operations (MLOps) Distributors List
  • Table 115. Research Programs/Design for This Report
  • Table 116. Key Data Information from Secondary Sources
  • Table 117. Key Data Information from Primary Sources

List of Figures

  • Figure 1. Machine Learning Operations (MLOps) Product Picture
  • Figure 2. Global Machine Learning Operations (MLOps) Sales Value, 2020 VS 2024 VS 2031 (US$ Million)
  • Figure 3. Global Machine Learning Operations (MLOps) Sales Value (2020-2031) & (US$ Million)
  • Figure 4. Machine Learning Operations (MLOps) Report Years Considered
  • Figure 5. Global Machine Learning Operations (MLOps) Players Revenue Ranking (2024) & (US$ Million)
  • Figure 6. The 5 and 10 Largest Companies in the World: Market Share by Machine Learning Operations (MLOps) Revenue in 2024
  • Figure 7. Machine Learning Operations (MLOps) Market Share by Company Type (Tier 1, Tier 2, and Tier 3): 2020 VS 2024
  • Figure 8. On-premise Picture
  • Figure 9. Cloud Picture
  • Figure 10. Others Picture
  • Figure 11. Global Machine Learning Operations (MLOps) Sales Value by Type (2020 VS 2024 VS 2031) & (US$ Million)
  • Figure 12. Global Machine Learning Operations (MLOps) Sales Value Market Share by Type, 2024 & 2031
  • Figure 13. Product Picture of BFSI
  • Figure 14. Product Picture of Healthcare
  • Figure 15. Product Picture of Retail
  • Figure 16. Product Picture of Manufacturing
  • Figure 17. Product Picture of Public Sector
  • Figure 18. Product Picture of Others
  • Figure 19. Global Machine Learning Operations (MLOps) Sales Value by Application (2020 VS 2024 VS 2031) & (US$ Million)
  • Figure 20. Global Machine Learning Operations (MLOps) Sales Value Market Share by Application, 2024 & 2031
  • Figure 21. North America Machine Learning Operations (MLOps) Sales Value (2020-2031) & (US$ Million)
  • Figure 22. North America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • Figure 23. Europe Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 24. Europe Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • Figure 25. Asia Pacific Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 26. Asia Pacific Machine Learning Operations (MLOps) Sales Value by Region (%), 2024 VS 2031
  • Figure 27. South America Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 28. South America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • Figure 29. Middle East & Africa Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 30. Middle East & Africa Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
  • Figure 31. Key Countries/Regions Machine Learning Operations (MLOps) Sales Value (%), (2020-2031)
  • Figure 32. United States Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 33. United States Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 34. United States Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 35. Europe Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 36. Europe Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 37. Europe Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 38. China Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 39. China Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 40. China Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 41. Japan Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 42. Japan Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 43. Japan Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 44. South Korea Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 45. South Korea Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 46. South Korea Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 47. Southeast Asia Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 48. Southeast Asia Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 49. Southeast Asia Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 50. India Machine Learning Operations (MLOps) Sales Value, (2020-2031) & (US$ Million)
  • Figure 51. India Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
  • Figure 52. India Machine Learning Operations (MLOps) Sales Value by Application (%), 2024 VS 2031
  • Figure 53. Machine Learning Operations (MLOps) Industrial Chain
  • Figure 54. Machine Learning Operations (MLOps) Manufacturing Cost Structure
  • Figure 55. Channels of Distribution (Direct Sales, and Distribution)
  • Figure 56. Bottom-up and Top-down Approaches for This Report
  • Figure 57. Data Triangulation
  • Figure 58. Key Executives Interviewed