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1868022

機器學習維運（MLOps）：2025-2031年全球市佔率及排名、總收入及需求預測

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個工作天內

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全球機器學習運維（MLOps）市場規模預計在 2024 年達到 19.76 億美元，預計到 2031 年將達到 225.17 億美元，2025 年至 2031 年的複合年成長率為 38.3%。

機器學習運作（MLOps）是指一套將機器學習模型的開發和運作緊密結合的實踐、工具和流程。它旨在將傳統的軟體開發DevOps概念引入機器學習領域，消除資料科學家、工程師和維運團隊之間的協作障礙。這使得機器學習的整個生命週期得以自動化和高效管理，涵蓋資料準備、模型訓練、模型評估、模型配置以及模型監控和維護等各個環節。透過MLOps，企業可以加速機器學習模型從實驗階段到生產階段的過渡，確保模型在生產環境中穩定運作，並持續最佳化，最終為企業創造顯著價值。

MLOps市場目前正經歷快速發展。隨著全球各產業數位轉型加速以及人工智慧和機器學習技術的應用日益廣泛，MLOps的重要性日益凸顯。

該市場具有以下特點：

應用範圍廣泛：在金融領域，銀行和保險公司利用它來最佳化風險評估模型並提高欺詐檢測效率；在醫療領域，它用於支持疾病預測和醫學影像診斷；在零售領域，它用於精準行銷和最佳化庫存管理；在製造業，它用於加強品管和預測設備故障。各行業對MLOps的積極探索和應用正在推動市場規模的持續擴大。

競爭格局正逐漸形成：AWS、Google Cloud 和 Microsoft Azure 等主要雲端運算供應商正憑藉其強大的雲端基礎架構和豐富的 AI 服務生態系統進軍機器學習維運 (MLOps) 領域。 DataRobot 和 H2O.ai 等機器學習平台專家在 MLOps 解決方案方面擁有深厚的技術專長。同時，越來越多Start-Ups創公司湧現，憑藉著創新技術和獨特的服務模式在細分市場中脫穎而出。整體競爭格局日趨多元化，各公司透過產品創新、策略合作、併購爭取市場佔有率。

多元化的需求因素：一方面，企業迫切需要提升機器學習計劃開發效率，縮短模型部署時間。傳統的機器學習計劃常面臨開發週期長、模型部署困難、維修成本高等挑戰。 MLOps 提供自動化流程和標準化工具，有效解決這些難題。另一方面，隨著資料量的爆炸性成長和模型複雜性的不斷提高，企業需要更專業的技術手段來管理模型的整個生命週期，確保模型效能的可靠性和穩定性。此外，跨部門協作的需求也推動了 MLOps 的應用，打破了資料科學團隊和 IT 維運團隊之間的溝通壁壘，實現了高效協作。

趨勢

與雲端原生技術的深度整合：展望未來，MLOps 將與雲端原生技術更加緊密地整合。雲端容器化架構（例如 Docker 和 Kubernetes 等容器編排管理工具）為 MLOps 提供高效的資源管理、靈活的部署方式和強大的可擴充性。透過利用雲端原生技術，企業可以輕鬆地在不同的雲端和混合雲端環境中快速部署和遷移機器學習模型，從而顯著降低基礎設施管理成本，同時提高整個系統的容錯性和可靠性。

持續推進自動化：自動化是機器學習運作維護（MLOps）的核心發展方向之一。從資料收集、清洗、標註到模型訓練、調優、評估，甚至模型部署和監控，每個環節都將高度自動化。例如，自動化機器學習（AutoML）技術將進一步發展，能夠自動選擇最優演算法、參數設定和資料預處理方法，顯著減少人工干預，提高機器學習計劃的開發效率。同時，系統將透過事件驅動的自動化流程即時監控模型效能。如果模型效能偏離預期或資料分佈發生變化，系統將自動觸發模型重新訓練或調整，確保模型始終保持最佳效能。

專注於模型可解釋性和合規性：隨著機器學習模型在金融、醫療、法律等關鍵業務領域的廣泛應用，模型可解釋性和合規性已成為重要關注點。未來的MLOps平台將整合更多可解釋性工具，幫助使用者理解模型的決策流程和輸出結果，進而增強使用者對模型的信任。此外，從資料隱私保護和法規遵循的角度來看，MLOps將提供更全面的解決方案，幫助企業在嚴格遵守相關法規（例如歐盟《一般資料保護規則》 (GDPR)）的同時，有效利用機器學習技術。

邊緣機器學習運維的崛起：隨著物聯網設備的激增以及對即時數據分析和處理需求的日益成長，邊緣運算在機器學習領域備受關注。邊緣機器學習運維旨在將機器學習模型的部署和運行從雲端擴展到邊緣設備，從而實現快速的本地資料處理和決策。這不僅可以降低資料傳輸延遲和網路頻寬消耗，還能增強資料安全性和隱私性。展望未來，邊緣機器學習維運將成為機器學習維運市場的關鍵成長領域，相關技術和產品也將持續湧現，以滿足各產業在邊緣場景下機器學習的多樣化應用需求。

本報告旨在按地區/國家、類型和應用對全球機器學習運維（MLOps）市場進行全面分析，重點關注總收入、市場佔有率和主要企業的排名。

機器學習維運（MLOps）市場規模、估算和預測以銷售收入為指標，以 2024 年為基準年，並包含 2020 年至 2031 年的歷史數據和預測數據。報告採用定量和定性分析相結合的方法，幫助讀者制定業務/成長策略，評估市場競爭格局，分析公司在當前市場中的地位，並就機器學習運維（MLOps）做出明智的業務決策。

市場區隔

公司

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

按類型分類的細分市場

  • 本地部署
  • 雲
  • 其他

應用領域

  • BFSI
  • 醫療保健
  • 零售
  • 製造業
  • 公共部門
  • 其他

按地區

  • 北美洲
    • 美國
    • 加拿大
  • 亞太地區
    • 中國
    • 日本
    • 韓國
    • 東南亞
    • 印度
    • 澳洲
    • 亞太其他地區
  • 歐洲
    • 德國
    • 法國
    • 英國
    • 義大利
    • 荷蘭
    • 北歐國家
    • 其他歐洲
  • 拉丁美洲
    • 墨西哥
    • 巴西
    • 其他拉丁美洲
  • 中東和非洲
    • 土耳其
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 其他中東和非洲地區

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