市場調查報告書
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1453748
2023-2030 年全球 MLOps 市場規模研究與預測(按組件、部署、組織規模、垂直和區域分析)Global MLOps Market Size Study & Forecast, by Component, by Deployment, by Organization Size, by Vertical, and Regional Analysis, 2023-2030 |
2022 年全球 MLOps 市場價值約為 11.9 億美元,預計在 2023-2030 年預測期內將以超過 39.7% 的健康成長率成長。 MLOps(即機器學習操作)包含旨在簡化生產環境中機器學習 (ML) 模型的部署、監控和管理的實務、工具和方法。它整合了 DevOps 的原則,以彌合資料科學和 IT 營運之間的差距。關鍵元件包括 ML 模型和資料的版本控制、自動化測試、CI/CD 管道、模型監控以及可擴展 ML 部署的基礎架構管理。 MLOps 促進資料科學家、工程師和營運團隊之間的協作,確保穩健、可靠且可擴展的 ML 模型,同時維護治理、合規性和安全標準。它在機器學習的實施中發揮著至關重要的作用,使組織能夠從投資中獲取價值,並推動醫療保健、金融和電子商務等各個領域的創新。人們越來越關注機器學習流程的標準化以實現有效的團隊合作,以及由於可監控性的提高而提高的效率,再加上生產力的提高和人工智慧實施速度的加快,這些都是推動全球市場需求的最突出因素。
此外,向基於雲端的基礎設施和工具的快速轉變有助於更廣泛的用戶更輕鬆地進行人工智慧開發和部署。根據Statista統計,2023年雲端IT基礎設施支出接近940億美元,預計到2026年將飆升至1,337億美元。公有雲端基礎設施的擴張仍然是IT支出成長的重要催化劑。主導市場格局的主要參與者包括戴爾科技、HPE、浪潮、聯想、IBM 和華為。 MLOps 平台利用雲端功能來提供可擴展、敏捷且可存取的解決方案。此外,金融領域機器學習使用的增加,以及企業對基於機器學習/人工智慧的專案需求的激增,在預測幾年內帶來了各種利潤豐厚的機會。然而,管理各種管道的難度和原始資料操縱的風險正在阻礙整個2023-2030年預測期內的市場成長。
全球 MLOps 市場研究涵蓋的關鍵區域包括亞太地區、北美、歐洲、拉丁美洲以及中東和非洲。北美在 2022 年佔據市場主導地位,因為該地區在人工智慧 (AI) 領域擁有強大的研發能力,並得到成熟經濟體、研究機構和領先人工智慧公司的支持。旨在增強客戶體驗和最佳化業務營運的先進技術的投資不斷增加,預計將在整個北美創造利潤豐厚的成長前景。此外,該地區擁有先進的人工智慧研發能力,並對人工智慧相關技術進行了大量投資。此外,北美也實施了有利於促進人工智慧發展的政策。例如,2022 年 12 月,開源公司 Allegro AI 宣佈在用戶群、收入和合作方面實現重大成長里程碑,進一步強調了該地區推動人工智慧創新的承諾。而亞太地區預計在預測年份將以最高的CAGR成長。雲端運算產業的快速成長,以及亞馬遜網路服務公司、微軟和谷歌等主要參與者的擴張,正在顯著推動整個地區的市場需求。隨著組織整合雲端基礎架構的可擴展性和靈活性,基於雲端的 MLOps 解決方案預計將在該地區廣泛採用。此外,亞太地區的政府和企業正在人工智慧和機器學習方面進行大量投資,從而推動了對能夠促進機器學習模型大規模開發和部署的 MLOps 解決方案的需求。
研究的目的是確定近年來不同細分市場和國家的市場規模,並預測未來幾年的價值。該報告旨在納入參與研究的國家內該行業的定性和定量方面。
該報告還提供了有關促進因素和挑戰等關鍵方面的詳細資訊,這些因素將決定市場的未來成長。此外,它還納入了利害關係人投資的微觀市場的潛在機會,以及對主要參與者的競爭格局和產品供應的詳細分析。
Global MLOps Market is valued at approximately USD 1.19 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 39.7% during the forecast period 2023-2030. MLOps, or Machine Learning Operations, encompasses practices, tools, and methodologies aimed at streamlining the deployment, monitoring, and management of Machine Learning (ML) models in production environments. It integrates principles from DevOps to bridge the gap between data science and IT operations. Key components include version control for ML models and data, automated testing, CI/CD pipelines, model monitoring, and infrastructure management for scalable ML deployments. MLOps facilitates collaboration between data scientists, engineers, and operations teams, ensuring robust, reliable, and scalable ML models while upholding governance, compliance, and security standards. It plays a vital role in operationalizing ML, enabling organizations to derive value from their investments and drive innovation across various domains such as healthcare, finance, and e-commerce. The growing focus on the standardization of ML processes for effective teamwork, and improved efficiency due to increased monitorability, coupled with increased productivity and quicker AI implementation are the most prominent factors that are propelling the market demand across the globe.
In addition, the rapid shift towards cloud-based infrastructure and tools facilitates easier access to AI development and deployment for a wider range of users. According to Statista, the expenditure on cloud IT infrastructure accounts for nearly USD 94 billion in 2023, which is anticipated to surge to USD 133.7 billion by 2026. The expansion of public cloud infrastructure remains a significant catalyst for IT spending growth. Key players dominating the market landscape comprise Dell Technologies, HPE, Inspur, Lenovo, IBM, and Huawei. MLOps platforms leverage cloud capabilities to provide scalable, agile, and accessible solutions. Moreover, the rise in the use of machine learning in the financial sector, as well as the surge in demand for ML/AI-based projects among businesses presents various lucrative opportunities over the forecast years. However, the difficulty in managing various pipelines and the risk of raw data manipulation is hindering the market growth throughout the forecast period of 2023-2030.
The key regions considered for the Global MLOps Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the region's robust research and development competencies in Artificial Intelligence (AI), supported by well-established economies, research institutions, and leading AI firms. The growing investment in advanced technologies aimed at augmenting customer experiences and optimizing business operations is poised to create lucrative growth prospects across North America. Additionally, the region boasts sophisticated AI research and development capabilities, with substantial investments in AI-related technologies. Furthermore, North America has implemented policies conducive to fostering AI development. For instance, in December 2022, Allegro AI, an open-source company, announced significant growth milestones in user base, revenue, and collaborations, further underscoring the region's commitment to advancing AI innovation. Whereas, Asia Pacific is expected to grow at the highest CAGR over the forecast years. The rapid growth of the cloud computing sector, along with key players like Amazon Web Services, Inc., Microsoft, and Google expanding their footprint are significantly propelling the market demand across the region. Cloud-based MLOps solutions are projected to witness substantial adoption in the region, as organizations integrate the scalability and flexibility of cloud infrastructure. Moreover, governments and enterprises across the APAC region are making significant investments in AI and machine learning, thereby fueling the demand for MLOps solutions capable of facilitating the development and deployment of machine learning models at scale.
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.
The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
List of tables and figures and dummy in nature, final lists may vary in the final deliverable
List of tables and figures and dummy in nature, final lists may vary in the final deliverable