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MLaaS(Machine Learning as a Service)市場レポート:コンポーネント、組織規模、用途、エンドユーザー、地域別、2024-2032

Machine Learning as a Service Market Report by Component, Organization Size, Application, End User, and Region 2024-2032

出版日: | 発行: IMARC | ページ情報: 英文 148 Pages | 納期: 2~3営業日

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価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=156.58円
MLaaS(Machine Learning as a Service)市場レポート:コンポーネント、組織規模、用途、エンドユーザー、地域別、2024-2032
出版日: 2024年03月02日
発行: IMARC
ページ情報: 英文 148 Pages
納期: 2~3営業日
  • 全表示
  • 概要
  • 図表
  • 目次
概要

世界のMLaaS(Machine Learning as a Service)市場規模は、2023年に75億米ドルに達しました。今後、IMARC Groupは、2024年から2032年にかけて27.24%の成長率(CAGR)を示し、2032年までに697億米ドルに達すると予測しています。組織における人工知能(AI)ソリューションへの需要の高まり、企業におけるクラウドコンピューティングの人気の高まり、世界のビジネスイニシアチブを加速するための自動化重視の高まりが、市場を推進する主な要因のいくつかです。

MLaaS(Machine Learning as a Service)は、クラウドベースのプラットフォームを通じて機械学習機能とインフラへのアクセスを提供する包括的なソリューションです。MLaaSは、ハードウェア、ソフトウェア、専門知識への多額の投資を必要とせずに、企業が機械学習の力を活用することを可能にします。MLaaSは、機械学習モデルの開発、デプロイ、管理を容易にするさまざまなサービス、ツール、リソースを提供します。MLaaSは、開発者やデータサイエンティストが簡単にアクセスし、利用できるように、事前に構築されたアルゴリズムやモデルを幅広く提供します。

世界のMLaaS(Machine Learning As A Service)市場

現在、大規模な社内インフラや専門知識を必要とせずに機械学習(ML)機能を利用できるMLaaSに対する需要の高まりが、市場の成長を後押ししています。これに加えて、効率性と生産性を高め、手作業によるエラーの発生を減らすために、さまざまなビジネス業務の自動化が進んでいることも、市場の成長を後押ししています。さらに、ディープラーニングや強化学習を含むMLアルゴリズムの進歩が進んでいることも、市場の見通しを良好なものにしています。これとは別に、データから貴重な洞察を引き出すために最先端の技術を活用する企業によるMLaaSの採用が増加していることも、市場の成長を支えています。さらに、ビジネスイニシアチブを加速させ、市場投入までの時間を短縮し、迅速な投資収益率(ROI)を実現するために自動化を重視する動きが高まっていることも、市場の成長に寄与しています。

MLaaS(Machine Learning as a Service)市場動向と促進要因:

人工知能(AI)ソリューションに対する需要の高まり

現在、さまざまな業界でAIソリューションの採用が増加していることが、MLaaSの需要を促進しています。企業がプロセスの最適化、顧客体験の向上、データからの実用的な洞察の獲得におけるAIの価値を認識するにつれて、MLaaSソリューションの需要が増加しています。企業はMLaaSを活用することで、ハードウェアや専門人材に多額の投資をすることなく、機械学習アルゴリズムのパワーを活用しています。また、MLaaSソリューションは、企業が簡単に実装できる、構築済みの機械学習モデルやデータ処理ツールも提供しています。これにより、中小企業でもAIを利用できるようになり、自社でAIを開発するためのリソースを多く持つ大企業と競争できるようになった。

クラウド・コンピューティングの人気の高まり

クラウド・コンピューティングの人気の高まりは、MLaaSの需要を大きく促進しています。クラウド・コンピューティングは、機械学習モデルを展開するための堅牢でスケーラブルな環境を提供するため、企業は高価なハードウェアやソフトウェアに投資することなく、最先端のML機能を利用することができます。これに加えて、クラウド・コンピューティングは、機械学習に不可欠な大量のデータの保存、処理、分析を容易にします。クラウドベースのMLaaSソリューションは、こうした膨大なデータセットを効率的に処理し、高速データ処理能力とリアルタイム分析を提供することで、迅速な意思決定を可能にし、企業の競争力を高める。さらに、クラウドプラットフォームは、異なる部門、あるいは異なる組織間で、機械学習モデルとデータの容易なコラボレーションとシームレスな共有を保証します。このようなコラボレーションの容易さは、企業がAI主導のデジタルトランスフォーメーションを推進する上で有益であり、MLaaSの導入拡大につながります。

データ世代の増加

現在、世界中でデータ生成量が増加しており、MLaaSの需要を大きく押し上げています。企業がより多くのデータを生成・収集するにつれて、そこから価値を引き出すMLの可能性も高まっています。MLaaSプロバイダーは、貴重な洞察を得て、情報に基づいたビジネス上の意思決定を行うために、これらのデータで訓練することができる既製の機械学習モデルを提供します。さらに、膨大なデータセットのリアルタイム分析は、ペースの速いデータ駆動型のシナリオにおいて極めて重要です。企業は、利用可能な最新の情報に基づいて迅速に意思決定を行う必要があります。大規模なデータセットをリアルタイムで処理する機能を備えたMLaaSプラットフォームは、ビジネスに即時の洞察を提供し、それによって業務効率を向上させ、迅速かつデータ主導の意思決定を可能にします。

目次

第1章 序文

第2章 調査範囲と調査手法

  • 調査目的
  • 利害関係者
  • データソース
    • 一次情報
    • 二次情報
  • 市場推定
    • ボトムアップアプローチ
    • トップダウンアプローチ
  • 調査手法

第3章 エグゼクティブサマリー

第4章 イントロダクション

  • 概要
  • 主要産業動向

第5章 MLaaS(Machine Learning as a Service)の世界市場

  • 市場概要
  • 市場実績
  • COVID-19の影響
  • 市場予測

第6章 市場内訳:コンポーネント別

  • ソフトウェア
    • 市場動向
    • 市場予測
  • サービス
    • 市場動向
    • 市場予測

第7章 市場内訳:組織規模別

  • 中小企業
    • 市場動向
    • 市場予測
  • 大企業
    • 市場動向
    • 市場予測

第8章 市場内訳:用途別

  • マーケティング・広告
    • 市場動向
    • 市場予測
  • 不正検知とリスク管理
    • 市場動向
    • 市場予測
  • 予測分析
    • 市場動向
    • 市場予測
  • 拡張現実と仮想現実
    • 市場動向
    • 市場予測
  • 自然言語処理
    • 市場動向
    • 市場予測
  • コンピュータビジョン
    • 市場動向
    • 市場予測
  • セキュリティと監視
    • 市場動向
    • 市場予測
  • その他
    • 市場動向
    • 市場予測

第9章 市場内訳:エンドユーザー別

  • IT・通信
    • 市場動向
    • 市場予測
  • 自動車
    • 市場動向
    • 市場予測
  • ヘルスケア
    • 市場動向
    • 市場予測
  • 航空宇宙・防衛
    • 市場動向
    • 市場予測
  • 小売
    • 市場動向
    • 市場予測
  • 政府機関
    • 市場動向
    • 市場予測
  • BFSI
    • 市場動向
    • 市場予測
  • その他
    • 市場動向
    • 市場予測

第10章 市場内訳:地域別

  • 北米
    • 米国
      • 市場動向
      • 市場予測
    • カナダ
      • 市場動向
      • 市場予測
  • アジア太平洋
    • 中国
      • 市場動向
      • 市場予測
    • 日本
      • 市場動向
      • 市場予測
    • インド
      • 市場動向
      • 市場予測
    • 韓国
      • 市場動向
      • 市場予測
    • オーストラリア
      • 市場動向
      • 市場予測
    • インドネシア
      • 市場動向
      • 市場予測
    • その他
      • 市場動向
      • 市場予測
  • 欧州
    • ドイツ
      • 市場動向
      • 市場予測
    • フランス
      • 市場動向
      • 市場予測
    • 英国
      • 市場動向
      • 市場予測
    • イタリア
      • 市場動向
      • 市場予測
    • スペイン
      • 市場動向
      • 市場予測
    • ロシア
      • 市場動向
      • 市場予測
    • その他
      • 市場動向
      • 市場予測
  • ラテンアメリカ
    • ブラジル
      • 市場動向
      • 市場予測
    • メキシコ
      • 市場動向
      • 市場予測
    • その他
      • 市場動向
      • 市場予測
  • 中東・アフリカ地域
    • 市場動向
    • 市場内訳:国別
    • 市場予測

第11章 SWOT分析

  • 概要
  • 強み
  • 弱み
  • 機会
  • 脅威

第12章 バリューチェーン分析

第13章 ポーターのファイブフォース分析

  • 概要
  • 買い手の交渉力
  • 供給企業の交渉力
  • 競合の程度
  • 新規参入業者の脅威
  • 代替品の脅威

第14章 価格分析

第15章 競合情勢

  • 市場構造
  • 主要企業
  • 主要企業のプロファイル
    • Amazon.com Inc.
    • Bigml Inc.
    • Fair Isaac Corporation
    • Google LLC(Alphabet Inc.)
    • H2O.ai Inc.
    • Hewlett Packard Enterprise Development LP
    • Iflowsoft Solutions Inc.
    • International Business Machines Corporation
    • Microsoft Corporation
    • MonkeyLearn
    • Sas Institute Inc.
    • Yottamine Analytics Inc.
図表

List of Figures

  • Figure 1: Global: Machine Learning as a Service Market: Major Drivers and Challenges
  • Figure 2: Global: Machine Learning as a Service Market: Sales Value (in Billion US$), 2018-2023
  • Figure 3: Global: Machine Learning as a Service Market Forecast: Sales Value (in Billion US$), 2024-2032
  • Figure 4: Global: Machine Learning as a Service Market: Breakup by Component (in %), 2023
  • Figure 5: Global: Machine Learning as a Service Market: Breakup by Organization Size (in %), 2023
  • Figure 6: Global: Machine Learning as a Service Market: Breakup by Application (in %), 2023
  • Figure 7: Global: Machine Learning as a Service Market: Breakup by End User (in %), 2023
  • Figure 8: Global: Machine Learning as a Service Market: Breakup by Region (in %), 2023
  • Figure 9: Global: Machine Learning as a Service (Software) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 10: Global: Machine Learning as a Service (Software) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 11: Global: Machine Learning as a Service (Services) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 12: Global: Machine Learning as a Service (Services) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 13: Global: Machine Learning as a Service (Small and Medium Size Enterprises) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 14: Global: Machine Learning as a Service (Small and Medium Size Enterprises) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 15: Global: Machine Learning as a Service (Large Enterprises) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 16: Global: Machine Learning as a Service (Large Enterprises) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 17: Global: Machine Learning as a Service (Marketing and Advertising) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 18: Global: Machine Learning as a Service (Marketing and Advertising) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 19: Global: Machine Learning as a Service (Fraud Detection and Risk Management) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 20: Global: Machine Learning as a Service (Fraud Detection and Risk Management) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 21: Global: Machine Learning as a Service (Predictive Analytics) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 22: Global: Machine Learning as a Service (Predictive Analytics) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 23: Global: Machine Learning as a Service (Augmented and Virtual Reality) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 24: Global: Machine Learning as a Service (Augmented and Virtual Reality) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 25: Global: Machine Learning as a Service (Natural Language Processing) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 26: Global: Machine Learning as a Service (Natural Language Processing) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 27: Global: Machine Learning as a Service (Computer Vision) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 28: Global: Machine Learning as a Service (Computer Vision) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 29: Global: Machine Learning as a Service (Security and Surveillance) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 30: Global: Machine Learning as a Service (Security and Surveillance) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 31: Global: Machine Learning as a Service (Other Applications) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 32: Global: Machine Learning as a Service (Other Applications) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 33: Global: Machine Learning as a Service (IT and Telecom) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 34: Global: Machine Learning as a Service (IT and Telecom) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 35: Global: Machine Learning as a Service (Automotive) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 36: Global: Machine Learning as a Service (Automotive) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 37: Global: Machine Learning as a Service (Healthcare) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 38: Global: Machine Learning as a Service (Healthcare) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 39: Global: Machine Learning as a Service (Aerospace and Defense) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 40: Global: Machine Learning as a Service (Aerospace and Defense) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 41: Global: Machine Learning as a Service (Retail) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 42: Global: Machine Learning as a Service (Retail) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 43: Global: Machine Learning as a Service (Government) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 44: Global: Machine Learning as a Service (Government) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 45: Global: Machine Learning as a Service (BFSI) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 46: Global: Machine Learning as a Service (BFSI) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 47: Global: Machine Learning as a Service (Other End Users) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 48: Global: Machine Learning as a Service (Other End Users) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 49: North America: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 50: North America: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 51: United States: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 52: United States: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 53: Canada: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 54: Canada: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 55: Asia-Pacific: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 56: Asia-Pacific: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 57: China: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 58: China: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 59: Japan: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 60: Japan: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 61: India: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 62: India: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 63: South Korea: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 64: South Korea: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 65: Australia: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 66: Australia: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 67: Indonesia: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 68: Indonesia: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 69: Others: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 70: Others: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 71: Europe: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 72: Europe: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 73: Germany: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 74: Germany: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 75: France: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 76: France: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 77: United Kingdom: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 78: United Kingdom: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 79: Italy: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 80: Italy: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 81: Spain: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 82: Spain: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 83: Russia: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 84: Russia: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 85: Others: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 86: Others: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 87: Latin America: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 88: Latin America: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 89: Brazil: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 90: Brazil: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 91: Mexico: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 92: Mexico: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 93: Others: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 94: Others: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 95: Middle East and Africa: Machine Learning as a Service Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 96: Middle East and Africa: Machine Learning as a Service Market: Breakup by Country (in %), 2023
  • Figure 97: Middle East and Africa: Machine Learning as a Service Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 98: Global: Machine Learning as a Service Industry: SWOT Analysis
  • Figure 99: Global: Machine Learning as a Service Industry: Value Chain Analysis
  • Figure 100: Global: Machine Learning as a Service Industry: Porter's Five Forces Analysis

List of Tables

  • Table 1: Global: Machine Learning as a Service Market: Key Industry Highlights, 2023 and 2032
  • Table 2: Global: Machine Learning as a Service Market Forecast: Breakup by Component (in Million US$), 2024-2032
  • Table 3: Global: Machine Learning as a Service Market Forecast: Breakup by Organization Size (in Million US$), 2024-2032
  • Table 4: Global: Machine Learning as a Service Market Forecast: Breakup by Application (in Million US$), 2024-2032
  • Table 5: Global: Machine Learning as a Service Market Forecast: Breakup by End User (in Million US$), 2024-2032
  • Table 6: Global: Machine Learning as a Service Market Forecast: Breakup by Region (in Million US$), 2024-2032
  • Table 7: Global: Machine Learning as a Service Market: Competitive Structure
  • Table 8: Global: Machine Learning as a Service Market: Key Players
目次
Product Code: SR112024A4820

The global machine learning as a service (MLaaS) market size reached US$ 7.5 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 69.7 Billion by 2032, exhibiting a growth rate (CAGR) of 27.24%during 2024-2032. The growing demand for artificial intelligence (AI) solutions among organizations, rising popularity of cloud computing among businesses, and increasing emphasis on automation to accelerate business initiatives worldwide are some of the major factors propelling the market.

Machine learning as a service (MLaaS) is a comprehensive solution that provides access to machine learning capabilities and infrastructure through a cloud-based platform. It enables organizations to leverage the power of machine learning without the need for significant investments in hardware, software, and specialized expertise. MLaaS offers a range of services, tools, and resources that facilitate the development, deployment, and management of machine learning models. It provides a wide array of pre-built algorithms and models that can be easily accessed and utilized by developers and data scientists.

Global Machine Learning As A Service (MLaaS) Market

At present, the increasing demand for MLaaS to access machine learning (ML) capabilities without the need for extensive in-house infrastructure and expertise is impelling the growth of the market. Besides this, the rising automation of various business operations to increase efficiency and productivity and reduce the occurrence of manual errors is propelling the growth of the market. In addition, the growing advancements in ML algorithms, including deep learning and reinforcement learning, are offering a favorable market outlook. Apart from this, the increasing employment of MLaaS by businesses to leverage cutting-edge techniques to extract valuable insights from their data is supporting the growth of the market. Additionally, the rising emphasis on automation to accelerate business initiatives, achieve faster time-to-time markets, and realize quicker returns on investments (ROI) is contributing to the growth of the market.

Machine Learning as a Service (MLaaS) Market Trends/Drivers:

Rising demand for artificial intelligence (AI) solutions

At present, the increasing employment of AI solutions across various industries is fueling the demand for MLaaS. As organizations recognize the value of AI in optimizing processes, enhancing customer experiences, and gaining actionable insights from data, the demand for MLaaS solutions is increasing. Businesses are leveraging MLaaS to harness the power of machine learning algorithms without the need for significant investments in hardware and specialized talent. MLaaS solutions also offer pre-built machine learning models and data handling tools which businesses can easily implement. It has made AI accessible to small and medium-sized businesses, enabling them to compete with larger companies that have more resources for developing AI in-house.

Growing popularity of cloud computing

The rising popularity of cloud computing is significantly driving the demand for MLaaS as it provides a robust and scalable environment for deploying machine learning models, enabling businesses to access cutting-edge ML capabilities without investing in expensive hardware or software. Besides this, cloud computing facilitates easy storage, processing, and analysis of large volumes of data, which are crucial for machine learning. Cloud-based MLaaS solutions can handle these vast datasets efficiently, providing high-speed data processing capabilities and real-time analytics, thereby enabling quick decision-making and creating a competitive edge for businesses. In addition, cloud platforms ensure easy collaboration and seamless sharing of machine learning models and data across different departments or even different organizations. This ease of collaboration can be instrumental in businesses to drive AI-driven digital transformation, thereby leading to increased uptake of MLaaS.

Increasing generation of data

Presently, there is an increase in data generation worldwide, which is significantly propelling the demand for MLaaS. As businesses generate and collect more data, the potential for ML to extract value from it also increases. MLaaS providers deliver ready-made machine learning models that can be trained on this data to gain valuable insights and make informed business decisions. Moreover, the real-time analysis of massive datasets is crucial in fast-paced, data-driven scenarios. Businesses need to make decisions quickly based on the latest information available. MLaaS platforms, equipped with the capability to process large datasets in real time, can provide businesses with immediate insights, thereby improving their operational efficiency and enabling swift and data-driven decision-making.

Machine Learning as a Service (MLaaS) Industry Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global machine learning as a service (MLaaS) market report, along with forecasts at the global and regional levels from 2024-2032. Our report has categorized the market based on component, organization size, application and end user.

Breakup by Component:

Software

Services

Services dominate the market

The report has provided a detailed breakup and analysis of the market based on the component. This includes software and services. According to the report, services represented the largest segment.

MLaaS providers offer pre-built and customizable machine learning models, which simplifies the adoption of machine learning technologies, especially for small and medium enterprises (SMEs) that may lack the resources or expertise to develop these models in-house. Developing and implementing machine learning models in-house can be quite expensive, considering the costs of hiring skilled data scientists, investing in robust hardware, and maintaining the necessary software. MLaaS provides a more cost-effective alternative as it operates on a pay-as-you-go model, allowing businesses to only pay for what they use. MLaaS providers also offer ongoing support and maintenance services, which can help businesses overcome any challenges they encounter when using the technology. This support can help businesses mitigate risks and ensure that their machine-learning models are performing optimally.

Breakup by Organization Size:

Small and Medium-sized Enterprises

Large Enterprises

Large enterprises hold the largest share in the market

A detailed breakup and analysis of the market based on the organization size has also been provided in the report. This includes small and medium-sized enterprises and large enterprises. According to the report, large enterprises accounted for the largest market share.

Large enterprises are increasingly turning to machine learning as a service (MLaaS) as it is a convenient, scalable, and cost-effective solution for implementing advanced machine learning capabilities, allowing large businesses to make data-driven decisions and gain a competitive edge. The vast amount of data generated by these enterprises necessitates efficient tools to extract meaningful insights, and MLaaS offers robust machine-learning models capable of processing this information swiftly and effectively. Moreover, in a dynamic business environment, large enterprises need to respond spontaneously to changing market conditions. With MLaaS, they can leverage real-time analytics to derive immediate insights from their data, enhancing their decision-making process and operational efficiency. This is particularly beneficial for industries that operate in fast-paced environments, such as finance, technology, and e-commerce.

Breakup by Application:

Marketing and Advertising

Fraud Detection and Risk Management

Predictive Analytics

Augmented and Virtual Reality

Natural Language Processing

Computer Vision

Security and Surveillance

Others

Marketing and advertising hold the biggest share in the market

A detailed breakup and analysis of the market based on the application have also been provided in the report. This includes marketing and advertising, fraud detection and risk management, predictive analytics, augmented and virtual reality, natural language processing, computer vision, security and surveillance, and others. According to the report, marketing and advertising accounted for the largest market share.

Marketing and advertising industries increasingly require machine learning as a service (MLaaS) due to its potential to transform their operations and customer engagements significantly. In these fields, understanding consumer behavior and preferences is of utmost importance, and the ability to analyze vast amounts of customer data is vital. MLaaS provides robust machine learning models that can process and analyze this data, offering valuable insights about customers, enabling personalized marketing, and improving target advertising. MLaaS is also used to segment customers based on various characteristics, enabling marketers to tailor their messages and offers to specific groups. It allows for precise targeting, which can significantly enhance the effectiveness of marketing campaigns.

Breakup by End User:

IT and Telecom

Automotive

Healthcare

Aerospace and Defense

Retail

Government

BFSI

Others

BFSI holds the maximum share of the market

A detailed breakup and analysis of the market based on the end user have also been provided in the report. This includes IT and telecom, automotive, healthcare, aerospace and defense, retail, government, BFSI, and others. According to the report, BFSI accounted for the largest market share.

The banking, financial services and insurance (BFSI) sector is relying on machine learning as a service (MLaaS) due to its transformative potential to streamline operations, enhance customer experiences, and bolster security measures. The BFSI sector deals with enormous amounts of data, and MLaaS provides an efficient way to process, analyze, and draw actionable insights from this data, enabling financial institutions to make informed decisions. MLaaS plays a pivotal role in personalizing customer experiences in the BFSI sector. By analyzing customer data, machine learning models can identify individual behaviors and preferences, enabling financial institutions to tailor their services to each customer's unique needs. Furthermore, by leveraging MLaaS, financial institutions can build predictive models that can alert them to potential fraud or risks in real-time, significantly enhancing their security measures and customer trust.

Breakup by Region:

North America

United States

Canada

Asia-Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America exhibits a clear dominance, accounting for the largest machine learning as a service (MLaaS) market share

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and Others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa.

North America held the biggest market share due to the rising number of businesses that are integrating AI and ML in their operations to achieve efficiency and scalability and minimize the involvement of humans.

Another contributing aspect is the rising generation of data through various online channels. Besides this, the increasing number of cyber threats and data breaches is propelling the growth of the market.

Asia Pacific is estimated to expand further in this domain due to the rising popularity of cloud computing and edge computing. Apart from this, the rising focus on automating various business operations is strengthening the growth of the market.

Competitive Landscape:

Key market players are investing in research operations to improve their machine-learning services. They are also providing cutting-edge machine learning tools and capabilities that are efficient, scalable, and easy to use. Top companies are entering into strategic partnerships with other tech companies, startups, and research institutions to deliver more comprehensive and innovative solutions. They are also focusing on providing training and certification programs to create a skilled workforce. Leading companies are taking initiatives to enhance the security features of their platforms. They are implementing stronger data encryption, enhancing access controls, and using machine learning to detect and respond to security threats.

The report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

Amazon.com Inc.

Bigml Inc.

Fair Isaac Corporation

Google LLC (Alphabet Inc.)

H2O.ai Inc.

Hewlett Packard Enterprise Development LP

Iflowsoft Solutions Inc.

International Business Machines Corporation

Microsoft Corporation

MonkeyLearn

Sas Institute Inc.

Yottamine Analytics Inc.

Recent Developments:

In March 2023, Amazon Web Services, and Amazon.com Inc. company, announced a collaboration with NVIDIA to build the world's most scalable, on-demand artificial intelligence (AI) infrastructure optimized to train large machine learning models and build generative AI applications.

In September 2018, Fair Isaac Corporation announced the launch of the latest version of FICO(R) Analytics Workbench(TM), which assists data scientists to understand the machine learning models behind AI-derived decisions.

In July 2023, International Business Machines Corporation announced the launch of Watsonx, which comprise three products to help businesses accelerate and scale AI and machine learning.

Key Questions Answered in This Report

  • 1. What was the size of the global machine learning as a service (MLaaS) market in 2023?
  • 2. What is the expected growth rate of the global machine learning as a service (MLaaS) market during 2024-2032?
  • 3. What are the key factors driving the global machine learning as a service (MLaaS) market?
  • 4. What has been the impact of COVID-19 on the global machine learning as a service (MLaaS) market?
  • 5. What is the breakup of the global machine learning as a service (MLaaS) market based on the component?
  • 6. What is the breakup of the global machine learning as a service (MLaaS) market based on organization size?
  • 7. What is the breakup of the global machine learning as a service (MLaaS) market based on the application?
  • 8. What is the breakup of the global machine learning as a service (MLaaS) market based on the end user?
  • 9. What are the key regions in the global machine learning as a service (MLaaS) market?
  • 10. Who are the key players/companies in the global machine learning as a service (MLaaS) market?

Table of Contents

1 Preface

2 Scope and Methodology

  • 2.1 Objectives of the Study
  • 2.2 Stakeholders
  • 2.3 Data Sources
    • 2.3.1 Primary Sources
    • 2.3.2 Secondary Sources
  • 2.4 Market Estimation
    • 2.4.1 Bottom-Up Approach
    • 2.4.2 Top-Down Approach
  • 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

  • 4.1 Overview
  • 4.2 Key Industry Trends

5 Global Machine Learning as a Service (MLaaS) Market

  • 5.1 Market Overview
  • 5.2 Market Performance
  • 5.3 Impact of COVID-19
  • 5.4 Market Forecast

6 Market Breakup by Component

  • 6.1 Software
    • 6.1.1 Market Trends
    • 6.1.2 Market Forecast
  • 6.2 Services
    • 6.2.1 Market Trends
    • 6.2.2 Market Forecast

7 Market Breakup by Organization Size

  • 7.1 Small and Medium-sized Enterprises
    • 7.1.1 Market Trends
    • 7.1.2 Market Forecast
  • 7.2 Large Enterprises
    • 7.2.1 Market Trends
    • 7.2.2 Market Forecast

8 Market Breakup by Application

  • 8.1 Marketing and Advertising
    • 8.1.1 Market Trends
    • 8.1.2 Market Forecast
  • 8.2 Fraud Detection and Risk Management
    • 8.2.1 Market Trends
    • 8.2.2 Market Forecast
  • 8.3 Predictive Analytics
    • 8.3.1 Market Trends
    • 8.3.2 Market Forecast
  • 8.4 Augmented and Virtual Reality
    • 8.4.1 Market Trends
    • 8.4.2 Market Forecast
  • 8.5 Natural Language Processing
    • 8.5.1 Market Trends
    • 8.5.2 Market Forecast
  • 8.6 Computer Vision
    • 8.6.1 Market Trends
    • 8.6.2 Market Forecast
  • 8.7 Security and Surveillance
    • 8.7.1 Market Trends
    • 8.7.2 Market Forecast
  • 8.8 Others
    • 8.8.1 Market Trends
    • 8.8.2 Market Forecast

9 Market Breakup by End User

  • 9.1 IT and Telecom
    • 9.1.1 Market Trends
    • 9.1.2 Market Forecast
  • 9.2 Automotive
    • 9.2.1 Market Trends
    • 9.2.2 Market Forecast
  • 9.3 Healthcare
    • 9.3.1 Market Trends
    • 9.3.2 Market Forecast
  • 9.4 Aerospace and Defense
    • 9.4.1 Market Trends
    • 9.4.2 Market Forecast
  • 9.5 Retail
    • 9.5.1 Market Trends
    • 9.5.2 Market Forecast
  • 9.6 Government
    • 9.6.1 Market Trends
    • 9.6.2 Market Forecast
  • 9.7 BFSI
    • 9.7.1 Market Trends
    • 9.7.2 Market Forecast
  • 9.8 Others
    • 9.8.1 Market Trends
    • 9.8.2 Market Forecast

10 Market Breakup by Region

  • 10.1 North America
    • 10.1.1 United States
      • 10.1.1.1 Market Trends
      • 10.1.1.2 Market Forecast
    • 10.1.2 Canada
      • 10.1.2.1 Market Trends
      • 10.1.2.2 Market Forecast
  • 10.2 Asia-Pacific
    • 10.2.1 China
      • 10.2.1.1 Market Trends
      • 10.2.1.2 Market Forecast
    • 10.2.2 Japan
      • 10.2.2.1 Market Trends
      • 10.2.2.2 Market Forecast
    • 10.2.3 India
      • 10.2.3.1 Market Trends
      • 10.2.3.2 Market Forecast
    • 10.2.4 South Korea
      • 10.2.4.1 Market Trends
      • 10.2.4.2 Market Forecast
    • 10.2.5 Australia
      • 10.2.5.1 Market Trends
      • 10.2.5.2 Market Forecast
    • 10.2.6 Indonesia
      • 10.2.6.1 Market Trends
      • 10.2.6.2 Market Forecast
    • 10.2.7 Others
      • 10.2.7.1 Market Trends
      • 10.2.7.2 Market Forecast
  • 10.3 Europe
    • 10.3.1 Germany
      • 10.3.1.1 Market Trends
      • 10.3.1.2 Market Forecast
    • 10.3.2 France
      • 10.3.2.1 Market Trends
      • 10.3.2.2 Market Forecast
    • 10.3.3 United Kingdom
      • 10.3.3.1 Market Trends
      • 10.3.3.2 Market Forecast
    • 10.3.4 Italy
      • 10.3.4.1 Market Trends
      • 10.3.4.2 Market Forecast
    • 10.3.5 Spain
      • 10.3.5.1 Market Trends
      • 10.3.5.2 Market Forecast
    • 10.3.6 Russia
      • 10.3.6.1 Market Trends
      • 10.3.6.2 Market Forecast
    • 10.3.7 Others
      • 10.3.7.1 Market Trends
      • 10.3.7.2 Market Forecast
  • 10.4 Latin America
    • 10.4.1 Brazil
      • 10.4.1.1 Market Trends
      • 10.4.1.2 Market Forecast
    • 10.4.2 Mexico
      • 10.4.2.1 Market Trends
      • 10.4.2.2 Market Forecast
    • 10.4.3 Others
      • 10.4.3.1 Market Trends
      • 10.4.3.2 Market Forecast
  • 10.5 Middle East and Africa
    • 10.5.1 Market Trends
    • 10.5.2 Market Breakup by Country
    • 10.5.3 Market Forecast

11 SWOT Analysis

  • 11.1 Overview
  • 11.2 Strengths
  • 11.3 Weaknesses
  • 11.4 Opportunities
  • 11.5 Threats

12 Value Chain Analysis

13 Porters Five Forces Analysis

  • 13.1 Overview
  • 13.2 Bargaining Power of Buyers
  • 13.3 Bargaining Power of Suppliers
  • 13.4 Degree of Competition
  • 13.5 Threat of New Entrants
  • 13.6 Threat of Substitutes

14 Price Analysis

15 Competitive Landscape

  • 15.1 Market Structure
  • 15.2 Key Players
  • 15.3 Profiles of Key Players
    • 15.3.1 Amazon.com Inc.
      • 15.3.1.1 Company Overview
      • 15.3.1.2 Product Portfolio
      • 15.3.1.3 Financials
      • 15.3.1.4 SWOT Analysis
    • 15.3.2 Bigml Inc.
      • 15.3.2.1 Company Overview
      • 15.3.2.2 Product Portfolio
    • 15.3.3 Fair Isaac Corporation
      • 15.3.3.1 Company Overview
      • 15.3.3.2 Product Portfolio
      • 15.3.3.3 Financials
      • 15.3.3.4 SWOT Analysis
    • 15.3.4 Google LLC (Alphabet Inc.)
      • 15.3.4.1 Company Overview
      • 15.3.4.2 Product Portfolio
      • 15.3.4.3 SWOT Analysis
    • 15.3.5 H2O.ai Inc.
      • 15.3.5.1 Company Overview
      • 15.3.5.2 Product Portfolio
    • 15.3.6 Hewlett Packard Enterprise Development LP
      • 15.3.6.1 Company Overview
      • 15.3.6.2 Product Portfolio
      • 15.3.6.3 Financials
      • 15.3.6.4 SWOT Analysis
    • 15.3.7 Iflowsoft Solutions Inc.
      • 15.3.7.1 Company Overview
      • 15.3.7.2 Product Portfolio
    • 15.3.8 International Business Machines Corporation
      • 15.3.8.1 Company Overview
      • 15.3.8.2 Product Portfolio
      • 15.3.8.3 Financials
      • 15.3.8.4 SWOT Analysis
    • 15.3.9 Microsoft Corporation
      • 15.3.9.1 Company Overview
      • 15.3.9.2 Product Portfolio
      • 15.3.9.3 Financials
      • 15.3.9.4 SWOT Analysis
    • 15.3.10 MonkeyLearn
      • 15.3.10.1 Company Overview
      • 15.3.10.2 Product Portfolio
    • 15.3.11 Sas Institute Inc.
      • 15.3.11.1 Company Overview
      • 15.3.11.2 Product Portfolio
      • 15.3.11.3 SWOT Analysis
    • 15.3.12 Yottamine Analytics Inc.
      • 15.3.12.1 Company Overview
      • 15.3.12.2 Product Portfolio