市場調查報告書
商品編碼
1137701

基於機器學習的服務 (MLaaS) 市場 - 增長、趨勢、COVID-19 影響和預測(2022-2027 年)

Machine Learning as a Service (MLaaS) Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

出版日期: | 出版商: Mordor Intelligence Pvt Ltd | 英文 167 Pages | 商品交期: 2-3個工作天內

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  • 全貌
  • 簡介
  • 目錄
簡介

在預測期內,基於機器學習的服務 (MLaaS) 市場預計將以 39.25% 的複合年增長率註冊。

主要亮點

  • 機器學習 (ML) 是人工智能 (AI) 的一個子領域,它使學習算法能夠通過統計方法進行分類和預測,以揭示數據挖掘項目中的關鍵見解。這些洞察力推動應用程序和業務決策,理想地影響關鍵增長指標。由於數據挖掘圍繞算法、模型複雜性、計算複雜性等展開,因此需要熟練的專業人員來開發這些解決方案。
  • 數據科學和人工智能的進步正在快速提高機器學習的性能。隨著公司看到這項技術的潛力,預計該技術的採用率將在預測期內增加。公司正在以基於訂閱的模式提供機器學習解決方案,使消費者更容易利用該技術。此外,現收現付的計費系統可實現靈活的響應。
  • Amazon將於 2021 年發布其首個機器學習 IDE SageMaker Studio。此應用程序提供了一個基於 Web 的界面,允許客戶在單一環境中為所有 ML 模型運行訓練測試。SageMaker Studio 提供對所有開發技術和工具的訪問,包括筆記本、調試工具、數據建模及其自動創建。
  • 市場上的主要參與者正在組織競賽,以訓練人工智能和安全社區使用實時人工智能係統處理關鍵場景。例如,在 2021 年 7 月,Microsoft將建立 MLSEC.IO,這是一個面向 AI 和安全社區的教育機器學習安全規避競賽 (MLSEC),以練習在現實世界中對關鍵 AI 系統的攻擊。該比賽由Microsoft、NVIDIA、CUJO AI、VM-Ray 和 MRG Effitas 主辦和共同贊助,將獎勵成功避開基於人工智能的惡意軟件和基於人工智能的網絡釣魚檢測器的競爭對手。
  • 機器學習初創公司擁有數百萬美元的機器學習投資。例如,2022 年 6 月,Inflection AI 獲得了總額為 2.25 億美元的最大的人工機器學習融資回合之一。被稱為機器學習和人工智能初創公司。它已從風險投資中獲得了 2.25 億美元的股權融資。這項 ML 投資將在不久的將來改進機器學習並實現直觀的人機界面。
  • 機器學習服務利用深度學習技術進行預測分析,以增強決策制定。然而,MLaaS 給 ML 模型所有者帶來了安全挑戰,給數據所有者帶來了數據隱私挑戰。數據所有者擔心他們在 MLaaS 平台上的數據的隱私和安全。另一方面,MLaaS 平台所有者擔心模型被冒充客戶的對手竊取。
  • COVID-19 大流行已導致許多公司加速遷移到公共雲。因為雲服務的彈性使他們能夠應對意外的服務需求高峰。遷移到雲端正在幫助公司重塑他們在 COVID-19 時期開展業務的方式。隨著對 AI 服務需求的增長,許多雲提供商提供 AIaaS 和 MLaaS。

主要市場趨勢

物聯網和自動化的日益普及推動了市場

  • 物聯網運營確保數千台或更多設備在企業網絡上正確、安全地運行,並且收集的數據及時準確。雖然複雜的後端分析引擎負責處理數據流的繁重工作,但確保數據質量通常留給過時的技術。為了確保對龐大的物聯網基礎設施的包容性,一些物聯網平台供應商引入了機器學習技術來增強運營管理能力。
  • 機器學習可以通過利用高級算法分析大量數據來發現物聯網數據中的隱藏模式。機器學習可以在關鍵流程中使用統計衍生的操作,用自動化系統補充或替代手動流程。基於機器學習的解決方案可以自動化物聯網數據建模過程,消除模型選擇、編碼和驗證等繁瑣乏味的任務。
  • 採用物聯網的小型企業可以顯著縮短耗時的機器學習過程。MLaaS 供應商可以更快地執行更多查詢並提供更多類型的分析,以從物聯網網絡中多個設備生成的大量數據緩存中提取更多可操作信息。
  • 根據 Zebra 的製造視覺研究,預計到 2022 年,基於物聯網和 RFID 的智能資產監控系統將超越傳統的基於電子表格的方法。根據Microsoft Corporation進行的一項調查,85% 的公司至少有一個 IIoT 用例項目。到 2021 年,94% 的受訪者表示他們將推進 IIoT 工作,而且這個數字可能會上升。這些示例可能會在不久的將來為 MLaaS 供應商提供機會。
  • 許多組織現在正在使用基於雲的技術來促進這些連接,這有利於數據傳輸。這使您組織中的所有員工都可以訪問數據,從而使您的公司更具成本效益。2021 年 5 月,Google Cloud發布了 Vertex AI,這是一個新的託管機器學習平台,允許用戶根據客戶需求維護和部署 AI 模型。

預計北美將佔據最大的市場份額

  • 北美擁有強大的創新生態系統,這得益於聯邦政府對先進技術的戰略投資,再加上來自世界知名研究機構的有遠見的科學家和企業家以及 MLaaS,因此預計將佔據很大的市場份額促進發展的
  • Mlaas 的大部分業務都在北美地區,在加拿大和美國等國家增長強勁。這些國家是大大小小的初創公司的所在地。因此,機器學習服務市場正在北美擴張。在技術突破和使用方面,北美是全球機器學習服務市場增長最快的地區。我們擁有投資機器學習服務的基礎設施和資金。此外,預計電信行業國防開支的增加和技術改進將推動市場在預測期內的增長。
  • 該地區還看到了 5G、物聯網和連接設備的大量採用。因此,通信服務提供商 (CSP) 必須通過虛擬化、網絡切片、新用例和服務需求有效地管理日益增加的複雜性。因此,管理網絡和服務的傳統方法不再可持續,預計將推動 MLaaS 解決方案。
  • 此外,該地區的科技巨頭,如Microsoft、Google、Amazon和 IBM,已經成為機器學習服務競賽的主要參與者。由於每家公司都有大規模的公共雲基礎設施和機器學習平台,它是一種人工智能約會模型,將被引入旨在在客戶服務、機器人流程自動化、營銷、分析和預測等所有領域利用人工智能的公司維護。將有可能實現支持培訓的機器學習類型的服務
  • 該地區的主要參與者正在使用新流程更新其平台,以為客戶提供無縫體驗,從而增加 MlaaS 市場的需求。例如,2021 年 12 月,BigML 將圖像處理添加到其 BigML 平台中,提供的功能增強了其解決圖像數據驅動的業務問題的能力,並具有出色的易用性。標記圖像數據、訓練和評估模型、預測、自動化端到端機器學習工作流程等。
  • 此外,2021 年 11 月,SAS 為其旗艦 SAS Viya 平台增加了對開源用戶的支持。SAS Viya 用於開源集成和實用程序。軟件用戶已經建立了一種 API 優先策略,通過機器學習推動數據準備過程。
  • 該地區的機器學習市場已經被雲改造,無服務器計算使開發人員能夠快速啟動機器學習應用程序。此外,信息服務是機器學習服務業務的驅動力。無服務器計算帶來的最大變化是無需擴展物理數據庫硬件。

競爭格局

市場整合的加劇加劇了Microsoft、IBM、Google和Amazon等知名企業之間的競爭。其他參與者正在積極擴大他們的產品組合和地域分佈,以佔領很大的市場份額。

  • 2022 年 2 月 - 電信巨頭 AT&T 和 AI 公司 H2O 合作推出面向企業的人工智能功能商店。它為協作、共享、重用和發現機器學習能力、加速人工智能項目的部署和提高投資回報率提供了一個存儲庫。
  • 2021 年 12 月 - AWS 宣布了 Amazon SageMaker 的 6 項新功能。這使機器學習更易於訪問且更具成本效益。這帶來了強大的新功能,例如用於創建準確機器學習預測的無代碼環境以及使用高技能註釋器進行更準確的數據標記。

其他福利

  • Excel 格式的市場預測 (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
  • 調查範圍

第二章研究方法論

第三章執行摘要

第四章市場洞察

  • 市場概況
  • 產業吸引力——波特五力分析
    • 新進入者的威脅
    • 買方議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭對手之間的競爭
  • 行業價值鏈分析
  • 評估 COVID-19 對市場的影響

第五章市場動態

  • 市場驅動力
    • 物聯網和自動化的採用率增加
    • 越來越多地採用基於雲的服務
  • 市場限制
    • 隱私和數據安全問題
    • 需要熟練的專業人員

第六章市場細分

  • 應用
    • 營銷/廣告
    • 預測性維護
    • 自動化網絡管理
    • 欺詐檢測和風險分析
    • 其他應用(NLP、情感分析、計算機視覺)
  • 組織規模
    • 中小企業
    • 大公司
  • 最終用戶
    • IT/通訊
    • 汽車
    • 衛生保健
    • 航空航天與國防
    • 零售
    • 政府機構
    • BFSI更多
    • 其他最終用戶(教育、媒體和娛樂、農業、貿易市場)
  • 按地區
    • 北美
    • 歐洲
    • 亞太地區
    • 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.

第八章投資分析

第九章 市場潛力

目錄
Product Code: 55039

The machine learning-as-a-service (MLaaS) market is expected to register a CAGR of 39.25% during the forecast period.

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.
  • 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 are offering machine learning solutions on a subscription-based model, making it easier for consumers to take advantage of this technology. In addition, it provides flexibility on a pay-as-you-use basis.
  • Amazon released SageMaker Studio, the first machine learning IDE, in 2021. This application provides a web-based interface through which clients can run all ML model training tests in a single environment. SageMaker Studio provides access to all development methods and tools, including notebooks, debugging tools, data modeling, and its automatic creation.
  • The significant players in the market are organizing competitions to train Ai and security communities to handle critical scenarios in real-time AI systems. For instance, In July 2021, Microsoft established MLSEC.IO, an educational Machine Learning Security Evasion Competition (MLSEC) for the AI and the security community to practice attacking critical AI systems in a realistic context. The competition, hosted and sponsored by Microsoft, NVIDIA, CUJO AI, VM-Ray, and MRG Effitas, awards competitors who successfully escape AI-based malware and AI-based phishing detectors.
  • 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 will 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 in the time of COVID-19. The need for AI services has grown, and many cloud providers offer AIaaS and MLaaS.

Key Market Trends

Increasing Adoption of IoT and Automation to Drive the Market

  • IoT operations ensure that the thousands or more devices run correctly and safely on an enterprise network and the data being collected is both timely and accurate. While sophisticated back-end analytics engines work on the heavy lifting of processing the data stream, ensuring data quality is often left to obsolete methodologies. To ensure the rein in sprawling IoT infrastructures, some IoT platform vendors are baking machine learning technology to boost their operations management capabilities.
  • 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 are 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 could rise, as 94% of respondents said they would pursue IIoT initiatives in 2021. These instcances may create opportunities for the 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 May 2021, Google Cloud unveiled Vertex AI, a new managed machine learning platform that allows users to maintain and deploy AI models based on client needs.

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.
  • 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 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 are updating their platform with new processes to offer seamless experiences to their clients, increasing the MlaaS market's demand. For instance, In December 2021, BigMl added Image Processing to the BigML platform, a feature that enhances their offering to solve image data-driven business problems with remarkable ease of use. It labels the image data, train and evaluate models, make predictions, and automate end-to-end machine learning workflows.
  • Moreover, In November 2021, SAS added support for open-source users to its flagship SAS Viya platform. SAS Viya is for open-source integration and utility. The software user established an API-first strategy that fueled a data preparation process with machine learning.
  • The region's ML marketplace is changing due to the cloud, and serverless computing makes it possible for 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 eliminating the need to scale physical database hardware.

Competitive Landscape

The high market consolidation has increased the competition among prominent players such as Microsoft, IBM, Google, and Amazon. To capture a significant share in the market, other players are actively expanding their product portfolios and geographical presence.

  • February 2022 - Telecom giant AT&T and AI company H2O have 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.
  • December 2021 - AWS announced six new Amazon SageMaker capabilities. This will make machine learning even more accessible and cost-effective. This brings together powerful new capabilities, including a no-code environment for creating accurate machine learning predictions and more accurate data labeling using highly skilled annotators.

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 Threat of New Entrants
    • 4.2.2 Bargaining Power of Buyers
    • 4.2.3 Bargaining Power of Suppliers
    • 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