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1444263

機器學習即服務 (MLaaS) - 市場佔有率分析、產業趨勢與統計、成長預測（2024 年 - 2029 年）

Machine Learning As A Service (MLaaS) - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 167 Pages | 商品交期: 2-3個工作天內

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簡介目錄

機器學習即服務市場規模預計到 2024 年將達到 713.4 億美元，預計到 2029 年將達到 3,093.7 億美元，在預測期內（2024-2029 年）CAGR為 34.10%。

機器學習即服務 (MLaaS) - 市場

主要亮點

  • 機器學習 (ML) 是人工智慧 (AI) 的一個子領域，它使訓練演算法能夠透過統計方法進行分類或預測，從而揭示資料探勘項目中的關鍵見解。這些見解推動應用程式和業務內的決策，理想情況下會影響關鍵成長指標。由於它圍繞著演算法、模型複雜性和計算複雜性展開，因此需要熟練的專業人員來開發這些解決方案。
  • 機器學習即服務 (MLaaS) 市場可能會在預測期內出現高速成長，因為 MLaaS 演算法用於尋找資料中的模式，且使用者不必擔心實際運算。 MLaaS是唯一結合行動應用、企業智慧、工業自動化和控制系統的全端人工智慧平台。
  • 隨著資料科學和人工智慧的進步，機器學習的效能快速提升。公司正在認知到這項技術的潛力，因此，預計該技術的採用率在預測期內將會增加。公司以基於訂閱的模式提供機器學習解決方案，使消費者更容易使用該技術。此外，它還提供依使用量付費的靈活性。
  • 此外，MLaaS 廣泛應用於詐欺偵測、供應鏈最佳化、風險分析、製造等領域。用戶可以從頭開始自由建立內部基礎設施，使管理和儲存資料變得更加容易。
  • 機器學習新創公司正在獲得數百萬美元的機器學習投資。例如，2022 年 6 月，Inflection AI 獲得了最大的人工機器學習融資輪之一，總額達 2.25 億美元。它被稱為機器學習和人工智慧新創公司。已獲得創投家2.25億美元股權融資。這項機器學習投資預計將改善機器學習，從而在不久的將來實現直覺的人機介面。
  • 機器學習即服務利用深度學習技術進行預測分析，以增強決策能力。然而，使用 MLaaS 為 ML 模型所有者帶來了安全課題，也為資料所有者帶來了資料隱私課題。資料所有者擔心 MLaaS 平台上資料的隱私和安全。相比之下，MLaaS 平台所有者擔心他們的模型可能會被冒充客戶的對手竊取。
  • COVID-19 大流行促使許多組織加速遷移到公有雲解決方案，因為雲端服務彈性可以滿足服務需求的意外高峰。遷移到雲端幫助公司在新冠肺炎 (COVID-19) 疫情期間重塑了開展業務的方式。對人工智慧服務的需求不斷成長，許多雲端供應商提供 AIaaS 和 MLaaS。

機器學習即服務 (MLAAS) 市場趨勢

擴大採用物聯網和自動化來推動市場

  • 物聯網營運可確保數千或更多設備在企業網路上正確、安全地運行，並且收集的資料及時、準確。雖然複雜的後端分析引擎負責資料流處理的主要部分，但確保資料品質的方法往往是過時的。一些物聯網平台供應商正在利用機器學習技術來提高其營運管理能力，以確保控制龐大的物聯網基礎設施。
  • 機器學習可以透過利用複雜的演算法分析大量資料來揭開物聯網資料中隱藏模式的神秘面紗。機器學習推理可以透過在關鍵流程中使用統計得出的操作的自動化系統來補充或取代手動流程。基於機器學習建構的解決方案可自動執行物聯網資料建模流程，從而消除模型選擇、編碼和驗證等迂迴且勞力密集的活動。
  • 採用物聯網的小型企業可以大幅節省耗時的機器學習流程。 MLaaS 供應商可以更快地進行更多查詢，提供更多類型的分析，以便從物聯網網路中多個裝置產生的大量資料快取中獲取更多可操作的資訊。
  • 根據Zebra 的製造願景研究，預計到2022 年，基於物聯網和RFID 的智慧資產監控系統的性能將優於基於電子表格的傳統方法。根據微軟公司進行的研究，85% 的企業至少擁有一個工業物連網用例項目。這一數字預計還會上升，因為 94% 的受訪者表示他們將在 2021 年推行 IIoT 計劃。這些情況可能會在不久的將來為 MLaaS 供應商創造機會。
  • 由於可以輕鬆形成這些連接，許多組織擴大使用基於雲端的技術，有利於資料傳輸。這使得組織中的每個員工都可以存取資料，從而提高公司的成本效率。 2023 年 4 月，Oracle Corporation 和 GitLab Inc. 宣布推出擴展 ML 和 AI 功能的新產品。客戶可以在 Oracle 雲端基礎架構 (OCI) 上使用支援 GPU 的 GitLab 執行程式執行 AI 和 ML 工作負載，並可在任何需要的地方（包括本機和多雲環境）部署雲端服務。

北美預計將佔據最大的市場佔有率

  • 由於強大的創新生態系統，在聯邦對先進技術的戰略投資的推動下，再加上來自全球知名研究機構的有遠見的科學家和企業家的存在，北美預計將在市場上佔據重要佔有率，這推動了發展MLaaS 的。
  • 例如，2023年5月，美國國家科學基金會（NSF）與高等教育機構、其他聯邦機構和其他利害關係人合作，宣布投資1.4億美元新建七個國家人工智慧研究所（AI） 。透過這項投資，政府旨在推廣人工智慧系統和技術，並在美國培養多元化的人工智慧勞動力，以推動對人工智慧相關機會和風險採取一致的方法。地方政府的此類投資將為所研究的市場創造新的成長機會。
  • 由於加拿大和美國等國家的顯著成長，北美地區佔據了 Mlaas 業務的大部分。這些國家擁有各種各樣的小型和大型新創公司。因此，機器學習即服務的市場正在北美不斷擴大。在技​​術突破和使用方面，北美是全球機器學習即服務市場成長最快的地區。它擁有投資機器學習即服務的基礎設施和資金。此外，國防支出的增加和電信業的技術改進可能會在整個預測期內促進市場成長。
  • 該地區也見證了 5G、物聯網和互聯設備的大幅成長。因此，通訊服務提供者 (CSP) 需要透過虛擬化、網路切片、新用例和服務需求來有效管理日益成長的複雜性。由於傳統的網路和服務管理方法不再永續，預計這將推動 MLaaS 解決方案的發展。
  • 此外，該地區的主要科技公司，如微軟、Google、亞馬遜和 IBM，已成為機器學習即服務競賽的主要參與者。由於每家公司都擁有相當大的公有雲端基礎設施和機器學習平台，因此對於那些希望將人工智慧用於從客戶服務到機器人流程自動化、行銷、分析、預測性維護等，以協助訓練正在部署的人工智慧資料模型。
  • 該地區的主要參與者專注於擴展業務，為客戶提供無縫體驗，從而增加 MlaaS 市場的需求。例如，2022 年 2 月，AWS 宣佈在全球擴展 AWS 本地區域。該公司宣布已在美國建成首批 16 個 AWS 本地開發區，並計劃在全球 26 個國家的 32 個新都會區推出新的 AWS 本地開發區。
  • 該地區的 ML 市場正在因雲端而發生變化，無伺服器運算使開發人員能夠快速啟動並運行 ML 應用程式。此外，機器學習即服務業務的主要驅動力是資訊服務。無伺服器運算帶來的最重大變化是消除了擴展實體資料庫硬體的需要。

機器學習即服務 (MLAAS) 產業概述

市場的高度整合加劇了微軟、IBM、Google和亞馬遜等知名企業之間的競爭。為了在機器學習即服務 (MLAAS) 市場中佔據重要佔有率，其他參與者正在積極擴展其產品組合和地理分佈。

2023 年 2 月，雲端原生服務證明者 Civo 宣布推出 Kubeflow 即服務（其新的機器學習託管服務），以改善開發人員體驗並減少從 ML 演算法獲取見解所需的時間和資源。透過此次發布，該公司旨在讓各種規模的組織都能使用機器學習。

2022年2月，電信巨擘AT&T與人工智慧公司H2O合作，推出了企業導向的人工智慧功能商店。這提供了一個用於協作、共享、重複使用和發現機器學習功能的儲存庫，以加快人工智慧專案部署並提高投資回報率。

額外的好處：

  • Excel 格式的市場估算 (ME) 表
  • 3 個月的分析師支持

目錄

第 1 章：簡介

  • 研究假設和市場定義
  • 研究範圍

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 買家的議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代產品的威脅
    • 競爭激烈程度
  • 產業價值鏈分析
  • COVID-19 對市場的影響評估

第 5 章：市場動態

  • 市場促進因素
    • 擴大採用物聯網和自動化
    • 擴大採用基於雲端的服務
  • 市場限制
    • 隱私和資料安全問題
    • 需要熟練的專業人員

第 6 章：市場區隔

  • 應用
    • 行銷與廣告
    • 預測性維護
    • 自動化網路管理
    • 詐欺偵測和風險分析
    • 其他應用（NLP、情緒分析和電腦視覺）
  • 組織規模
    • 中小企業
    • 大型企業
  • 最終用戶
    • 資訊科技和電信
    • 汽車
    • 衛生保健
    • 航太和國防
    • 零售
    • 政府
    • BFSI
    • 其他最終用戶（教育、媒體和娛樂、農業和貿易市場）
  • 地理
    • 北美洲
    • 歐洲
    • 亞太
    • 世界其他地區

第 7 章：競爭格局

  • 公司簡介
    • Microsoft Corporation
    • IBM Corporation
    • Google LLC
    • SAS Institute Inc.
    • Fair Isaac Corporation (FICO)
    • Hewlett Packard Enterprise Company
    • Yottamine Analytics LLC
    • Amazon Web Services Inc.
    • BigML Inc.
    • Iflowsoft Solutions Inc.
    • Monkeylearn Inc.
    • Sift Science Inc.
    • H2O.ai Inc.

第 8 章：投資分析

第 9 章：市場的未來

簡介目錄
Product Code: 55039

The Machine Learning As A Service Market size is estimated at USD 71.34 billion in 2024, and is expected to reach USD 309.37 billion by 2029, growing at a CAGR of 34.10% during the forecast period (2024-2029).

Machine Learning As A Service (MLaaS) - Market

Key Highlights

  • Machine learning (ML) is a subfield of artificial intelligence (AI) that enables training algorithms to make classifications or predictions through statistical methods, uncovering critical insights within data mining projects. These insights drive decision-making within applications and businesses, ideally impacting key growth metrics. Since it revolves around algorithms, model complexity, and computational complexity, it requires skilled professionals to develop these solutions.
  • The machine learning as a service (MLaaS) market will likely witness high growth over the forecast period as MLaaS algorithms are used to find patterns in the data, and users don't have to worry about the actual calculations. MLaaS is the only full-stack AI platform combining mobile applications, enterprise intelligence, industrial automation, and control systems.
  • With advancements in data science and artificial intelligence, the performance of machine learning accelerated at a rapid pace. Companies are identifying the potential of this technology, and therefore, the adoption rate of the same is expected to increase over the forecast period. Companies offer machine learning solutions on a subscription-based model, making it easier for consumers to use this technology. In addition, it provides flexibility on a pay-as-you-use basis.
  • Moreover, MLaaS is widely used in fraud detection, supply chain optimization, risk analytics, manufacturing, and others. Users can freely build internal infrastructure from scratch, making managing and storing your data easier.
  • The ML startups are receiving fundings millions of dollars of ML investment. For instance, In June 2022, Inflection AI secured one of the largest artificial machine learning funding rounds, totaling USD 225 million. It is referred to as a machine learning and AI startup. It has obtained USD 225 million in equity financing from venture capitalists. This ML investment is expected to improve machine learning, allowing for intuitive human-computer interfaces in the near future.
  • Machine learning-as-a-service leverages deep learning techniques for predictive analytics to enhance decision-making. However, using MLaaS introduces security challenges for ML model owners and data privacy challenges for data owners. Data owners are concerned about the privacy and safety of their data on MLaaS platforms. In contrast, MLaaS platform owners worry that their models may be stolen by adversaries who pose as clients.
  • The COVID-19 pandemic caused many organizations to accelerate their migrations to public cloud solutions since cloud service elasticity can meet unexpected spikes in service demand. Migrations to the cloud helped companies reinvent the way they conduct their businesses during the time of COVID-19. The need for AI services has grown, and many cloud providers offer AIaaS and MLaaS.

Machine Learning as a Services(MLAAS) Market Trends

Increasing Adoption of IoT and Automation to Drive the Market

  • IoT operations ensure that thousands or more devices run correctly and safely on an enterprise network and that the data being collected is timely and accurate. While sophisticated back-end analytics engines work on the major bit of data stream processing, ensuring data quality is often left to obsolete methodologies. Some IoT platform vendors are baking machine learning technology to boost their operations management capabilities to ensure rein in sprawling IoT infrastructures.
  • Machine learning may demystify the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. ML inference may supplement or replace manual processes with automated systems using statistically derived actions in critical processes. Solutions built on ML automate the IoT data modeling process, thus, removing the circuitous and labor-intensive activities of model selection, coding, and validation.
  • Small businesses adopting IoT may significantly save on the time-consuming machine learning process. MLaaS vendors may conduct more queries more quickly, providing more types of analysis to get more actionable information from vast caches of data generated by multiple devices in the IoT network.
  • As per Zebra's Manufacturing Vision Study, smart asset monitoring systems based on IoT and RFID were predicted to outperform traditional, spreadsheet-based approaches by 2022. According to research conducted by Microsoft Corporation, 85% of businesses have at least one IIoT use case project. This figure was expected to rise, as 94% of respondents said they would pursue IIoT initiatives in 2021. These instances may create opportunities for MLaaS vendors in the near future.
  • The increasing use of cloud-based technology in many organizations benefits data transfer due to the ease with which these connections may be formed. This allows every employee in an organization to access data, increasing a company's cost efficiency. In April 2023, Oracle Corporation and GitLab Inc. announced the availability of a new offering that expands ML and AI functionalities. Customers can run AI and ML workloads with GPU-enabled GitLab runners on Oracle Cloud Infrastructure (OCI) and get access to deploy cloud services wherever needed, including on-premises and multi-cloud environments.

North America is Expected to Hold the Largest Market Share

  • North America is expected to hold a significant share in the market owing to the robust innovation ecosystem, fueled by strategic federal investments into advanced technology, complemented by the presence of visionary scientists and entrepreneurs coming together from globally renowned research institutions, which has propelled the development of MLaaS.
  • For instance, in May 2023, The U.S. National Science Foundation (NSF), in collaboration with higher education institutions, other federal agencies, and other stakeholders, announced to invest USD 140 million to establish seven new National Artificial Intelligence Research Institutes (AI) institutes. Through this investment, the government aims to promote AI systems and technologies and develop a diverse AI workforce in the United States to advance a cohesive approach to AI-related opportunities and risks. Such investments by the regional government will create new growth opportunities for the studied market.
  • Because of remarkable growth in countries such as Canada and the United States, the North American region accounts for most of Mlaas business. These countries are home to a wide diversity of small and large start-ups. As a result, the market for machine learning as a service is expanding in North America. Regarding technological breakthroughs and use, North America is the fastest-growing region worldwide in the machine learning as a service market. It has the infrastructure and funds to invest in machine learning as a service. Furthermore, increased defense spending and technical improvements in the telecommunications industry will likely boost market growth throughout the forecast period.
  • The region also witnessed a significant proliferation of 5G, IoT, and connected devices. As a result, communications service providers (CSPs) need to manage an ever-growing complexity efficiently through virtualization, network slicing, new use cases, and service requirements. This is expected to drive MLaaS solutions as traditional network and service management approaches are no longer sustainable.
  • Moreover, major technology firms in the region, such as Microsoft, Google, Amazon, and IBM, have stepped up as major players in the ML-as-a-service race. Because each of the companies has a sizeable public cloud infrastructure and ML platforms, this allows the companies to make machine learning-as-a-service a reality for those looking to use AI for everything ranging from customer service to robotic process automation, marketing, analytics, predictive maintenance, etc., to assist in training the AI date models being deployed.
  • The key players in this region focus on expanding to offer their clients seamless experiences, increasing the MlaaS market's demand. For instance, In February 2022, AWS announced the global expansion of AWS local zones. It told the completion of its first 16 AWS Local Zones in the United States, and it plans to launch new AWS Local Zones in 32 new metropolitan areas in 26 countries worldwide.
  • The region's ML marketplace is changing due to the cloud, and serverless computing allows developers to get ML applications up and running quickly. Additionally, the prime driver of the ML-as-a-service business is information services. The most significant change serverless computing has brought in is eliminating the need to scale physical database hardware.

Machine Learning as a Services(MLAAS) Industry Overview

The high market consolidation has increased the competition among prominent players such as Microsoft, IBM, Google, and Amazon. To capture a significant share of the Machine Learning-as-a-Service (MLAAS) Market, other players are actively expanding their product portfolios and geographical presence.

In February 2023, Civo, the cloud-native service prover, announced to launch of Kubeflow as a service, its new Machine Learning managed service, to improve the developer experience and reduce the time and resources required to gain insights from ML algorithms. Through this launch, the company aims to make ML accessible to all sizes of organizations.

In February 2022, Telecom giant AT&T and AI company H2O collaborated and launched an artificial intelligence feature store for enterprises. This delivers a repository for collaborating, sharing, reusing, and discovering machine learning features to speed AI project deployments and improve ROI.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Buyers
    • 4.2.2 Bargaining Power of Suppliers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Industry Value Chain Analysis
  • 4.4 Assessment of Impact of COVID-19 on the Market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Adoption of IoT and Automation
    • 5.1.2 Increasing Adoption of Cloud-based Services
  • 5.2 Market Restraints
    • 5.2.1 Privacy and Data Security Concerns
    • 5.2.2 Need for Skilled Professionals

6 MARKET SEGMENTATION

  • 6.1 Application
    • 6.1.1 Marketing and Advertisement
    • 6.1.2 Predictive Maintenance
    • 6.1.3 Automated Network Management
    • 6.1.4 Fraud Detection and Risk Analytics
    • 6.1.5 Other Applications (NLP, Sentiment Analysis, and Computer Vision)
  • 6.2 Organization Size
    • 6.2.1 Small and Medium Enterprises
    • 6.2.2 Large Enterprises
  • 6.3 End-User
    • 6.3.1 IT and Telecom
    • 6.3.2 Automotive
    • 6.3.3 Healthcare
    • 6.3.4 Aerospace and Defense
    • 6.3.5 Retail
    • 6.3.6 Government
    • 6.3.7 BFSI
    • 6.3.8 Other End-Users (Education, Media and Entertainment, Agriculture, and Trading Market Place)
  • 6.4 Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia-Pacific
    • 6.4.4 Rest of the World

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Microsoft Corporation
    • 7.1.2 IBM Corporation
    • 7.1.3 Google LLC
    • 7.1.4 SAS Institute Inc.
    • 7.1.5 Fair Isaac Corporation (FICO)
    • 7.1.6 Hewlett Packard Enterprise Company
    • 7.1.7 Yottamine Analytics LLC
    • 7.1.8 Amazon Web Services Inc.
    • 7.1.9 BigML Inc.
    • 7.1.10 Iflowsoft Solutions Inc.
    • 7.1.11 Monkeylearn Inc.
    • 7.1.12 Sift Science Inc.
    • 7.1.13 H2O.ai Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET