機械学習の自動化市場：オートメーションタイプ、展開、アプリケーション別-2025-2030年の世界予測

機械学習の自動化市場の2023年の市場規模は16億3,000万米ドルで、2024年には22億1,000万米ドルに達すると予測され、CAGR 35.70％で成長し、2030年には138億8,000万米ドルに達すると予測されています。

機械学習の自動化（AutoML）は、モデルの選択、トレーニング、チューニングのプロセスを自動化することで、洗練された機械学習ツールへのアクセスを民主化し、データサイエンスにおける変革を象徴しています。AutoMLの必要性は、従来の機械学習アプローチでは専門的な知識と多大な時間が必要であった、ヘルスケア、金融、小売など様々な分野におけるデータ駆動型の洞察に対する需要の高まりから生まれました。AutoMLのアプリケーションは、予測分析、異常検知、顧客セグメンテーションなど多岐にわたり、これらの業界全体の意思決定プロセスを強化します。最終用途の範囲は、必ずしも社内に専門知識を持たずにAI機能の統合を検討しているあらゆる規模の企業を網羅しており、既存企業と新興新興企業の両方に機会を提供しています。市場の洞察によると、AutoMLの成長は、データ量の増加、データサイエンティストに対するニーズの高まり、拡張可能で効率的なAIモデルに対する需要が原動力となっています。主なビジネスチャンスは、AutoMLがネットワーク運用や自律機能を最適化できる通信や自動車など、急速なデジタル変革に直面している業界にあります。しかし、この市場は、既存システムとの統合の課題や、大規模な初期データ準備の必要性といった限界に直面しています。さらに、モデルの透明性と解釈可能性の確保という課題もあり、これは特に規制分野では信頼を得るために極めて重要です。簡素化されたデータ前処理方法を提供し、透明性の問題に対処するイノベーションは、市場の成長を大きく促進する可能性があります。さらに、ユーザーフレンドリーなインターフェースへの投資や、説明可能なAI機能の拡張は、研究開発の機が熟している分野です。AutoML市場の性質はダイナミックであり、急速な技術進歩とビジネスニーズの移り変わりが顕著で、成長の大きな可能性を提供しています。競争力を維持するためには、継続的なイノベーションと、新たなAI規制や倫理基準への適応が必要であり、ビジネス戦略が技術的な可能性と責任あるAI利用の両方に合致するようにしなければならないです。

市場力学：急速に進化する機械学習の自動化市場の主要市場インサイトを公開

機械学習の自動化市場は、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、戦略的意思決定、新たなビジネスチャンスの獲得を行うことができます。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るとともに、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
  • 意思決定のためのデータ主導の洞察に対する需要の高まり
  • 機械学習能力の民主化の拡大
• 市場抑制要因
  • AutoMLプラットフォームに関連する解釈可能性と透明性の問題
• 市場機会
  • 人工知能（AI）と機械学習（ML）技術の進歩
  • 機械学習モデルの開発を強化するDevOpsプラクティスとのAutoMLの統合の拡大
• 市場の課題
  • AutoMLプラットフォームのセキュリティとプライバシーに関する懸念

ポーターのファイブフォース：機械学習の自動化市場をナビゲートする戦略ツール

ポーターのファイブフォースフレームワークは、機械学習の自動化市場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：機械学習の自動化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、機械学習の自動化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。

市場シェア分析機械学習の自動化市場における競合情勢の把握

機械学習の自動化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・マトリックス機械学習の自動化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、機械学習の自動化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。

戦略分析と推奨機械学習の自動化市場における成功への道筋を描く

機械学習の自動化市場の戦略分析は、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
    • 意思決定のためのデータに基づく洞察の需要の増加
    • 機械学習機能の民主化の拡大
  • 抑制要因
    • AutoMLプラットフォームに関連する解釈可能性と透明性の問題
  • 機会
    • 人工知能（AI）と機械学習（ML）技術の進歩
    • 機械学習モデルの開発を強化するDevOpsプラクティスとAutoMLの統合が進む
  • 課題
    • AutoMLプラットフォームの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 機械学習の自動化市場自動化タイプ別

• データ処理
• 機能エンジニアリング
• モデリング
• 視覚化

第7章 機械学習の自動化市場：展開別

• クラウド
• オンプレミス

第8章 機械学習の自動化市場：用途別

• 自動車、輸送、物流
• 銀行、金融サービス、保険
• 政府と防衛
• ヘルスケアとライフサイエンス
• ITおよび通信
• メディアとエンターテイメント

第9章 南北アメリカの機械学習の自動化市場

• 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機械学習の自動化市場

• 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・アフリカの機械学習の自動化市場

• 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

• Aible, Inc.
• Akkio Inc.
• Altair Engineering Inc.
• Alteryx
• Amazon Web Services, Inc.
• Automated Machine Learning Ltd.
• BigML, Inc.
• Databricks, Inc.
• Dataiku
• DataRobot, Inc.
• Google LLC by Alphabet Inc.
• H2O.ai, Inc.
• Hewlett Packard Enterprise Company
• InData Labs Group Limited
• Intel Corporation
• International Business Machines Corporation
• Microsoft Corporation
• Oracle Corporation
• QlikTech International AB
• Runai Labs Ltd.
• Salesforce, Inc.
• SAS Institute Inc.
• ServiceNow, Inc.
• SparkCognition, Inc.
• STMicroelectronics
• Tata Consultancy Services Limited
• TAZI AI
• Tellius, Inc.
• Weidmuller Limited
• Wolfram
• Yellow.ai

LIST OF FIGURES

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

LIST OF TABLES

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

The Automated Machine Learning Market was valued at USD 1.63 billion in 2023, expected to reach USD 2.21 billion in 2024, and is projected to grow at a CAGR of 35.70%, to USD 13.88 billion by 2030.

Automated Machine Learning (AutoML) represents a transformative advancement in data science, democratizing access to sophisticated machine learning tools by automating the process of model selection, training, and tuning. The necessity for AutoML arises from the increasing demand for data-driven insights across various sectors such as healthcare, finance, and retail, where traditional machine learning approaches require expert knowledge and substantial time investment. AutoML's applications are extensive, including predictive analytics, anomaly detection, customer segmentation, and more, enhancing decision-making processes across these industries. The end-use scope encompasses businesses of all sizes looking to integrate AI capabilities without necessarily having in-house expertise, offering opportunities for both established enterprises and emerging startups. Market insights indicate that the growth of AutoML is driven by the growing data volume, the rising need for data scientists, and the demand for scalable, efficient AI models. Key opportunities lie in industries facing rapid digital transformation, such as telecommunications and automotive, where AutoML can optimize network operations or autonomous functionalities. However, the market faces limitations such as integration challenges with existing systems and the need for significant initial data preparation. Moreover, there are challenges in ensuring model transparency and interpretability, which are crucial for gaining trust, especially in regulated sectors. Innovations that offer simplified data-preprocessing methods and address transparency issues can significantly propel market growth. Furthermore, investing in user-friendly interfaces and expanding explainable AI capabilities are areas ripe for research and development. The nature of the AutoML market is dynamic, marked by rapid technological advancements and shifting business needs, offering substantial potential for growth. Staying competitive involves continuous innovation and adaptation to emerging AI regulations and ethical standards, ensuring that business strategies align with both technological potential and responsible AI use.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Automated Machine Learning Market

The Automated Machine Learning 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
  • Increasing demand for data-driven insights for decision-making
  • Expanding democratization of machine learning capabilities
• Market Restraints
  • Interpretability and transparency issues associated with AutoML platforms
• Market Opportunities
  • Advancements in artificial intelligence (AI) and machine learning (ML) technologies
  • Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
• Market Challenges
  • Security and privacy concerns of AutoML platforms

Porter's Five Forces: A Strategic Tool for Navigating the Automated Machine Learning Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Automated Machine Learning 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 Automated Machine Learning Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Automated Machine Learning 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 Automated Machine Learning Market

A detailed market share analysis in the Automated Machine Learning 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 Automated Machine Learning Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Automated Machine Learning 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 Automated Machine Learning Market

A strategic analysis of the Automated Machine Learning 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 Automated Machine Learning Market, highlighting leading vendors and their innovative profiles. These include Aible, Inc., Akkio Inc., Altair Engineering Inc., Alteryx, Amazon Web Services, Inc., Automated Machine Learning Ltd., BigML, Inc., Databricks, Inc., Dataiku, DataRobot, Inc., Google LLC by Alphabet Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, InData Labs Group Limited, Intel Corporation, International Business Machines Corporation, Microsoft Corporation, Oracle Corporation, QlikTech International AB, Runai Labs Ltd., Salesforce, Inc., SAS Institute Inc., ServiceNow, Inc., SparkCognition, Inc., STMicroelectronics, Tata Consultancy Services Limited, TAZI AI, Tellius, Inc., Weidmuller Limited, Wolfram, and Yellow.ai.

Market Segmentation & Coverage

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

• Based on Automation Type, market is studied across Data Processing, Feature Engineering, Modeling, and Visualization.
• Based on Deployment, market is studied across Cloud and On-premises.
• Based on Application, market is studied across Automotive, Transportations, and Logistics, Banking, Financial Services, and Insurance, Government & Defense, Healthcare & Life Sciences, It & Telecommunications, and Media & Entertainment.
• 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. Increasing demand for data-driven insights for decision-making
    • 5.1.1.2. Expanding democratization of machine learning capabilities
  • 5.1.2. Restraints
    • 5.1.2.1. Interpretability and transparency issues associated with AutoML platforms
  • 5.1.3. Opportunities
    • 5.1.3.1. Advancements in artificial intelligence (AI) and machine learning (ML) technologies
    • 5.1.3.2. Growing integration of AutoML with DevOps practices that enhance the development of machine learning models
  • 5.1.4. Challenges
    • 5.1.4.1. Security and privacy concerns of AutoML platforms
• 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. Automated Machine Learning Market, by Automation Type

• 6.1. Introduction
• 6.2. Data Processing
• 6.3. Feature Engineering
• 6.4. Modeling
• 6.5. Visualization

7. Automated Machine Learning Market, by Deployment

• 7.1. Introduction
• 7.2. Cloud
• 7.3. On-premises

8. Automated Machine Learning Market, by Application

• 8.1. Introduction
• 8.2. Automotive, Transportations, and Logistics
• 8.3. Banking, Financial Services, and Insurance
• 8.4. Government & Defense
• 8.5. Healthcare & Life Sciences
• 8.6. It & Telecommunications
• 8.7. Media & Entertainment

9. Americas Automated Machine Learning Market

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

10. Asia-Pacific Automated Machine Learning 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 Automated Machine Learning 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. Aible, Inc.
• 2. Akkio Inc.
• 3. Altair Engineering Inc.
• 4. Alteryx
• 5. Amazon Web Services, Inc.
• 6. Automated Machine Learning Ltd.
• 7. BigML, Inc.
• 8. Databricks, Inc.
• 9. Dataiku
• 10. DataRobot, Inc.
• 11. Google LLC by Alphabet Inc.
• 12. H2O.ai, Inc.
• 13. Hewlett Packard Enterprise Company
• 14. InData Labs Group Limited
• 15. Intel Corporation
• 16. International Business Machines Corporation
• 17. Microsoft Corporation
• 18. Oracle Corporation
• 19. QlikTech International AB
• 20. Runai Labs Ltd.
• 21. Salesforce, Inc.
• 22. SAS Institute Inc.
• 23. ServiceNow, Inc.
• 24. SparkCognition, Inc.
• 25. STMicroelectronics
• 26. Tata Consultancy Services Limited
• 27. TAZI AI
• 28. Tellius, Inc.
• 29. Weidmuller Limited
• 30. Wolfram
• 31. Yellow.ai