Machine-Learning-as-a-Service市場：コンポーネント、アプリケーション、エンドユーザー別-2025-2030年の世界予測

Machine-Learning-as-a-Service市場は、2023年に214億8,000万米ドルと評価され、2024年には280億米ドルに達すると予測され、CAGR 30.40％で成長し、2030年には1,377億8,000万米ドルになると予測されています。

Machine-Learning-as-a-Service（MLaaS）とは、データサイエンスの深い専門知識や大規模なインフラ投資を必要とせずに、包括的な機械学習ツール、技術、アプリケーションを企業に提供するクラウドベースのプラットフォームを指します。このサービスは、高度な分析へのアクセスを民主化し、様々な業界がビッグデータ分析、予測分析、複雑な意思決定プロセスに高度なアルゴリズムを活用できるようにするために不可欠です。その用途はヘルスケア、金融、小売、製造などの分野にまたがり、不正検知、パーソナライズされたマーケティング、顧客インサイト、業務効率化などの機能を促進します。最終用途の範囲には、AIをワークフローにシームレスに統合し、革新的な製品やサービスの市場投入までの時間を短縮しようとする企業も含まれます。

MLaaS市場の主な成長要因には、データ拡散の増加、クラウド導入の推進、AI主導型ソリューションに対する需要の高まりなどがあります。企業はデータ主導の考察を通じて競合優位性を確保しようとしており、これがMLaaSプラットフォームの需要を促進しています。特に、業界特有の課題に合わせたニッチソリューションの開発、モデルの説明可能性の向上、プライバシー保護の強化に課題があります。企業は、堅牢なサイバーセキュリティ対策に投資し、新興市場を取り込むために多言語対応を拡大することで利益を得ることができます。

成長を妨げる課題としては、データ・プライバシーに対する懸念、規制上の課題、複雑な出力を解釈する熟練した専門家の不足などが挙げられます。さらに、MLaaSソリューションはしばしば既存のインフラとの統合の課題に直面します。これらを克服するために、企業は、ITコンサルタント会社との提携を通じて、より容易な統合メカニズムを備えたユーザーフレンドリーなプラットフォームの開発に注力すべきです。

イノベーションは、自動機械学習（AutoML）、エッジコンピューティングの統合、信頼構築と規制遵守を容易にするモデルの透明性強化の調査を通じて促進することができます。さらに、学術界と産業界のコラボレーションを促進することで、特定のアプリケーションに適した斬新なアルゴリズムを生み出すことができると思われます。急速な技術の進歩や消費者の需要パターンの変化など、市場の性質は依然としてダイナミックです。これらの要因を戦略的にナビゲートし、継続的な学習と適応性を優先することで、企業はMLaaSの潜在能力を最大限に活用し、この急成長市場での足場を確保することができます。

市場力学：急速に進化するMachine-Learning-as-a-Service市場の主要市場インサイトを公開

Machine-Learning-as-a-Service市場は、需要と供給のダイナミックな相互作用によって変貌を遂げています。このような市場力学の進化を理解することで、企業は十分な情報に基づいた投資決定、戦略的決定の精緻化、そして新たなビジネスチャンスの獲得に備えることができます。これらの動向を包括的に把握することで、企業は政治的、地理的、技術的、社会的、経済的な領域にわたる様々なリスクを軽減することができ、また、消費者行動とそれが製造コストや購買動向に与える影響をより明確に理解することができます。

• 市場促進要因
  • IoTと自動化の採用の増加
  • クラウドベースのサービスの利用拡大
  • 複数の業界におけるパフォーマンスと業務効率の改善ニーズ
• 市場抑制要因
  • 訓練を受けた専門家の不足
• 市場機会
  • コグニティブ・コンピューティング、ニューラルネットワーク、ディープラーニング技術、人工知能（AI）の統合による技術の進歩
  • ヘルスケア業界における投資とコラボレーションの拡大
• 市場の課題
  • データ・セキュリティとプライバシーに関する懸念

ポーターの5つの力：Machine-Learning-as-a-Service市場をナビゲートする戦略ツール

ポーターの5つの力フレームワークは、市場情勢の競合情勢を理解するための重要なツールです。ポーターのファイブフォース・フレームワークは、企業の競争力を評価し、戦略的機会を探るための明確な手法を提供します。このフレームワークは、企業が市場内の勢力図を評価し、新規事業の収益性を判断するのに役立ちます。これらの洞察により、企業は自社の強みを活かし、弱みに対処し、潜在的な課題を回避することができ、より強靭な市場でのポジショニングを確保することができます。

PESTLE分析：Machine-Learning-as-a-Service市場における外部からの影響の把握

外部マクロ環境要因は、Machine-Learning-as-a-Service市場の業績ダイナミクスを形成する上で極めて重要な役割を果たします。政治的、経済的、社会的、技術的、法的、環境的要因の分析は、これらの影響をナビゲートするために必要な情報を提供します。PESTLE要因を調査することで、企業は潜在的なリスクと機会をよりよく理解することができます。この分析により、企業は規制、消費者の嗜好、経済動向の変化を予測し、先を見越した積極的な意思決定を行う準備ができます。

市場シェア分析Machine-Learning-as-a-Service市場における競合情勢の把握

Machine-Learning-as-a-Service市場の詳細な市場シェア分析により、ベンダーの業績を包括的に評価することができます。企業は、収益、顧客ベース、成長率などの主要指標を比較することで、競争上のポジショニングを明らかにすることができます。この分析により、市場の集中、断片化、統合の動向が明らかになり、ベンダーは競争が激化する中で自社の地位を高める戦略的意思決定を行うために必要な知見を得ることができます。

FPNVポジショニング・マトリックスMachine-Learning-as-a-Service市場におけるベンダーのパフォーマンス評価

FPNVポジショニングマトリックスは、Machine-Learning-as-a-Service市場においてベンダーを評価するための重要なツールです。このマトリックスにより、ビジネス組織はベンダーのビジネス戦略と製品満足度に基づき評価することで、目標に沿った十分な情報に基づいた意思決定を行うことができます。4つの象限によってベンダーを明確かつ正確にセグメント化し、戦略目標に最適なパートナーやソリューションを特定することができます。

戦略分析と推奨Machine-Learning-as-a-Service市場における成功への道筋を描く

Machine-Learning-as-a-Service市場の戦略分析は、世界市場でのプレゼンス強化を目指す企業にとって不可欠です。主要なリソース、能力、業績指標を見直すことで、企業は成長機会を特定し、改善に取り組むことができます。このアプローチにより、競合情勢における課題を克服し、新たなビジネスチャンスを活かして長期的な成功を収めるための体制を整えることができます。

本レポートでは、主要な注目分野を網羅した市場の包括的な分析を提供しています：

1.市場の浸透度：現在の市場環境の詳細なレビュー、主要企業による広範なデータ、市場でのリーチと全体的な影響力の評価。

2.市場の開拓度：新興市場における成長機会を特定し、既存分野における拡大可能性を評価し、将来の成長に向けた戦略的ロードマップを提供します。

3.市場の多様化：最近の製品発売、未開拓の地域、業界の主要な進歩、市場を形成する戦略的投資を分析します。

4.競合の評価と情報：競合情勢を徹底的に分析し、市場シェア、事業戦略、製品ポートフォリオ、認証、規制当局の承認、特許動向、主要企業の技術進歩などを検証します。

5.製品開発およびイノベーション：将来の市場成長を促進すると期待される最先端技術、研究開発活動、製品イノベーションをハイライトしています。

また、利害関係者が十分な情報を得た上で意思決定できるよう、重要な質問にも答えています：

1.現在の市場規模と今後の成長予測は？

2.最高の投資機会を提供する製品、セグメント、地域はどこか？

3.市場を形成する主な技術動向と規制の影響とは？

4.主要ベンダーの市場シェアと競合ポジションは？

5.ベンダーの市場参入・撤退戦略の原動力となる収益源と戦略的機会は何か？

目次

第1章 序文

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 市場の概要

第5章 市場洞察

• 市場力学
  • 促進要因
    • IoTと自動化の導入増加
    • クラウドベースのサービスの利用増加
    • さまざまな業界でパフォーマンスと運用効率を向上させる必要がある
  • 抑制要因
    • 訓練を受けた専門家の不足
  • 機会
    • 認知コンピューティング、ニューラルネットワーク、ディープラーニング技術、人工知能（AI）を統合した技術の進歩
    • ヘルスケア業界における投資と協力の拡大
  • 課題
    • データセキュリティとプライバシーに関する懸念
• 市場セグメンテーション分析
• ポーターのファイブフォース分析
• PESTEL分析
  • 政治的
  • 経済
  • 社交
  • 技術的
  • 法律上
  • 環境

第6章 Machine-Learning-as-a-Service市場：コンポーネント別

• サービス
• ソフトウェア

第7章 Machine-Learning-as-a-Service市場：用途別

• 拡張現実と仮想現実
• 不正検出とリスク管理
• マーケティングと広告
• 予測分析
• セキュリティと監視

第8章 Machine-Learning-as-a-Service市場：エンドユーザー別

• BFSI
• ヘルスケアとライフサイエンス
• 製造業
• 小売り
• 通信

第9章 南北アメリカのMachine-Learning-as-a-Service市場

• アルゼンチン
• ブラジル
• カナダ
• メキシコ
• 米国

第10章 アジア太平洋地域のMachine-Learning-as-a-Service市場

• オーストラリア
• 中国
• インド
• インドネシア
• 日本
• マレーシア
• フィリピン
• シンガポール
• 韓国
• 台湾
• タイ
• ベトナム

第11章 欧州・中東・アフリカのMachine-Learning-as-a-Service市場

• デンマーク
• エジプト
• フィンランド
• フランス
• ドイツ
• イスラエル
• イタリア
• オランダ
• ナイジェリア
• ノルウェー
• ポーランド
• カタール
• ロシア
• サウジアラビア
• 南アフリカ
• スペイン
• スウェーデン
• スイス
• トルコ
• アラブ首長国連邦
• 英国

第12章 競合情勢

• 市場シェア分析2023
• FPNVポジショニングマトリックス, 2023
• 競合シナリオ分析
• 戦略分析と提言

企業一覧

• Amazon.com Inc.
• AT&T Inc.
• BigML, Inc.
• Fair Isaac Corporation
• Google LLC
• H2O.ai
• Hewlett Packard Enterprise Company
• IBM Corp.
• Iflowsoft Solutions Inc.
• Microsoft Corporation
• Monkeylearn Inc.
• SAS Institute Inc.
• Sift Science Inc.
• Yottamine Analytics, LLC

LIST OF FIGURES

• FIGURE 1. MACHINE-LEARNING-AS-A-SERVICE MARKET RESEARCH PROCESS
• FIGURE 2. MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2023 VS 2030
• FIGURE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2030 (USD MILLION)
• FIGURE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 6. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2023 VS 2030 (%)
• FIGURE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2023 VS 2030 (%)
• FIGURE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2023 VS 2030 (%)
• FIGURE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 12. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 13. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 14. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2023 VS 2030 (%)
• FIGURE 15. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 16. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 17. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 18. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
• FIGURE 19. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
• FIGURE 20. MACHINE-LEARNING-AS-A-SERVICE MARKET SHARE, BY KEY PLAYER, 2023
• FIGURE 21. MACHINE-LEARNING-AS-A-SERVICE MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

• TABLE 1. MACHINE-LEARNING-AS-A-SERVICE MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
• TABLE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2030 (USD MILLION)
• TABLE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 6. MACHINE-LEARNING-AS-A-SERVICE MARKET DYNAMICS
• TABLE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICES, BY REGION, 2018-2030 (USD MILLION)
• TABLE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AUGMENTED & VIRTUAL REALITY, BY REGION, 2018-2030 (USD MILLION)
• TABLE 12. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY FRAUD DETECTION & RISK MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
• TABLE 13. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MARKETING & ADVERTISING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 14. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 15. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SECURITY & SURVEILLANCE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 16. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 17. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY REGION, 2018-2030 (USD MILLION)
• TABLE 18. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE & LIFE SCIENCES, BY REGION, 2018-2030 (USD MILLION)
• TABLE 19. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 20. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY REGION, 2018-2030 (USD MILLION)
• TABLE 21. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY TELECOM, BY REGION, 2018-2030 (USD MILLION)
• TABLE 22. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 23. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 24. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 25. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 26. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 27. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 28. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 29. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 30. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 31. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 32. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 33. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 34. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 35. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 36. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 37. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 38. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 39. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 40. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 41. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
• TABLE 42. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 43. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 44. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 45. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 46. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 47. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 48. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 49. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 50. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 51. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 52. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 53. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 54. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 55. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 56. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 57. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 58. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 59. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 60. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 61. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 62. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 63. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 64. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 65. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 66. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 67. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 68. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 69. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 70. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 71. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 72. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 73. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 74. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 75. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 76. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 77. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 78. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 79. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 80. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 81. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 82. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 83. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 84. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 85. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 86. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 87. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 88. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 89. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 90. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 91. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 92. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 93. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 94. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 95. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 96. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 97. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 98. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 99. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 100. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 101. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 102. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 103. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 104. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 105. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 106. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 107. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 108. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 109. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 110. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 111. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 112. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 113. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 114. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 115. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 116. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 117. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 118. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 119. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 120. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 121. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 122. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 123. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 124. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 125. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 126. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 127. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 128. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 129. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 130. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 131. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 132. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 133. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 134. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 135. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 136. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 137. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 138. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 139. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 140. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 141. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 142. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 143. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 144. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 145. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 146. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
• TABLE 147. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 148. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
• TABLE 149. MACHINE-LEARNING-AS-A-SERVICE MARKET SHARE, BY KEY PLAYER, 2023
• TABLE 150. MACHINE-LEARNING-AS-A-SERVICE MARKET, FPNV POSITIONING MATRIX, 2023

The Machine-Learning-as-a-Service Market was valued at USD 21.48 billion in 2023, expected to reach USD 28.00 billion in 2024, and is projected to grow at a CAGR of 30.40%, to USD 137.78 billion by 2030.

Machine-Learning-as-a-Service (MLaaS) refers to a cloud-based platform offering comprehensive machine learning tools, techniques, and applications for businesses without requiring in-depth expertise in data science or extensive infrastructure investment. This service is essential for democratizing access to advanced analytics, enabling various industries to leverage sophisticated algorithms for big data analysis, predictive analytics, and complex decision-making processes. Its application spans across sectors such as healthcare, finance, retail, and manufacturing, facilitating functions like fraud detection, personalized marketing, customer insights, and operational efficiency enhancement. The end-use scope includes companies seeking to integrate AI into their workflow seamlessly, reducing time-to-market for innovative products and services.

Key growth factors for the MLaaS market include increasing data proliferation, a push towards cloud adoption, and rising demand for AI-driven solutions. Organizations are striving for competitive advantages through data-driven insights, which is propelling demand for MLaaS platforms. Opportunities exist particularly in developing niche solutions tailored to industry-specific challenges, improving model explainability, and enhancing privacy protections. Companies can benefit by investing in robust cybersecurity measures and expanding multi-language support to capture emerging markets.

Limitations hindering growth include concerns over data privacy, regulatory challenges, and a shortage of skilled professionals to interpret complex outputs. Additionally, MLaaS solutions often face integration challenges with existing infrastructure. To overcome these, companies should focus on developing user-friendly platforms with easier integration mechanisms, possibly through partnerships with IT consultancies.

Innovation can be spurred through research in automated machine learning (AutoML), edge computing integration, and enhanced model transparency which can build trust and ease regulatory compliance. Moreover, fostering collaborations between academia and industry could yield novel algorithms suited for specific applications. The nature of the market remains dynamic, with rapid technological advancements and shifts in consumer demand patterns. By strategically navigating these factors and prioritizing continual learning and adaptability, businesses can harness MLaaS's full potential and secure their foothold in this burgeoning market.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Machine-Learning-as-a-Service Market

The Machine-Learning-as-a-Service Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Rising adoption of IoT and automation
  • Growing usage of cloud-based services
  • Need to improve performance and operational efficiency in the several industry
• Market Restraints
  • Lack of trained professionals
• Market Opportunities
  • Advancements in technologies with the integration of cognitive computing, neural networks, deep learning technologies, and artificial intelligence (AI)
  • Growing investments and collaboration in the healthcare Industry
• Market Challenges
  • Data security and privacy concerns

Porter's Five Forces: A Strategic Tool for Navigating the Machine-Learning-as-a-Service Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Machine-Learning-as-a-Service Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Machine-Learning-as-a-Service Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Machine-Learning-as-a-Service Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Machine-Learning-as-a-Service Market

A detailed market share analysis in the Machine-Learning-as-a-Service Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Machine-Learning-as-a-Service Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Machine-Learning-as-a-Service Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Machine-Learning-as-a-Service Market

A strategic analysis of the Machine-Learning-as-a-Service Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Machine-Learning-as-a-Service Market, highlighting leading vendors and their innovative profiles. These include Amazon.com Inc., AT&T Inc., BigML, Inc., Fair Isaac Corporation, Google LLC, H2O.ai, Hewlett Packard Enterprise Company, IBM Corp., Iflowsoft Solutions Inc., Microsoft Corporation, Monkeylearn Inc., SAS Institute Inc., Sift Science Inc., and Yottamine Analytics, LLC.

Market Segmentation & Coverage

This research report categorizes the Machine-Learning-as-a-Service Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Component, market is studied across Services and Software.
• Based on Application, market is studied across Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising, Predictive Analytics, and Security & Surveillance.
• Based on End User, market is studied across BFSI, Healthcare & Life Sciences, Manufacturing, Retail, and Telecom.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Rising adoption of IoT and automation
    • 5.1.1.2. Growing usage of cloud-based services
    • 5.1.1.3. Need to improve performance and operational efficiency in the several industry
  • 5.1.2. Restraints
    • 5.1.2.1. Lack of trained professionals
  • 5.1.3. Opportunities
    • 5.1.3.1. Advancements in technologies with the integration of cognitive computing, neural networks, deep learning technologies, and artificial intelligence (AI)
    • 5.1.3.2. Growing investments and collaboration in the healthcare Industry
  • 5.1.4. Challenges
    • 5.1.4.1. Data security and privacy concerns
• 5.2. Market Segmentation Analysis
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Machine-Learning-as-a-Service Market, by Component

• 6.1. Introduction
• 6.2. Services
• 6.3. Software

7. Machine-Learning-as-a-Service Market, by Application

• 7.1. Introduction
• 7.2. Augmented & Virtual Reality
• 7.3. Fraud Detection & Risk Management
• 7.4. Marketing & Advertising
• 7.5. Predictive Analytics
• 7.6. Security & Surveillance

8. Machine-Learning-as-a-Service Market, by End User

• 8.1. Introduction
• 8.2. BFSI
• 8.3. Healthcare & Life Sciences
• 8.4. Manufacturing
• 8.5. Retail
• 8.6. Telecom

9. Americas Machine-Learning-as-a-Service Market

• 9.1. Introduction
• 9.2. Argentina
• 9.3. Brazil
• 9.4. Canada
• 9.5. Mexico
• 9.6. United States

10. Asia-Pacific Machine-Learning-as-a-Service Market

• 10.1. Introduction
• 10.2. Australia
• 10.3. China
• 10.4. India
• 10.5. Indonesia
• 10.6. Japan
• 10.7. Malaysia
• 10.8. Philippines
• 10.9. Singapore
• 10.10. South Korea
• 10.11. Taiwan
• 10.12. Thailand
• 10.13. Vietnam

11. Europe, Middle East & Africa Machine-Learning-as-a-Service Market

• 11.1. Introduction
• 11.2. Denmark
• 11.3. Egypt
• 11.4. Finland
• 11.5. France
• 11.6. Germany
• 11.7. Israel
• 11.8. Italy
• 11.9. Netherlands
• 11.10. Nigeria
• 11.11. Norway
• 11.12. Poland
• 11.13. Qatar
• 11.14. Russia
• 11.15. Saudi Arabia
• 11.16. South Africa
• 11.17. Spain
• 11.18. Sweden
• 11.19. Switzerland
• 11.20. Turkey
• 11.21. United Arab Emirates
• 11.22. United Kingdom

12. Competitive Landscape

• 12.1. Market Share Analysis, 2023
• 12.2. FPNV Positioning Matrix, 2023
• 12.3. Competitive Scenario Analysis
• 12.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Amazon.com Inc.
• 2. AT&T Inc.
• 3. BigML, Inc.
• 4. Fair Isaac Corporation
• 5. Google LLC
• 6. H2O.ai
• 7. Hewlett Packard Enterprise Company
• 8. IBM Corp.
• 9. Iflowsoft Solutions Inc.
• 10. Microsoft Corporation
• 11. Monkeylearn Inc.
• 12. SAS Institute Inc.
• 13. Sift Science Inc.
• 14. Yottamine Analytics, LLC