自動機械学習（AutoML）市場：オファリング（ソリューション＆サービス）、アプリケーション（データ処理、モデル選択、ハイパーパラメータ最適化＆チューニング、フィーチャーエンジニアリング、モデルアンサンブル）、業種、地域別 - 2028年までの世界予測

Automated Machine Learning (AutoML) Market by Offering (Solutions & Services), Application (Data Processing, Model Selection, Hyperparameter Optimization & Tuning, Feature Engineering, Model Ensembling), Vertical and Region - Global Forecast to 2028

自動機械学習の市場は、2023年の10億米ドルから2028年には64億米ドルまで、予測期間中に44.6%のCAGRで成長すると予測されます。説明可能なAIは、機械学習モデルがどのように予測を行うかについて透明性を提供する... もっと見る

出版社	出版年月	電子版価格	ページ数	図表数	言語
MarketsandMarkets マーケッツアンドマーケッツ	2023年5月12日	US$4,950 シングルユーザライセンスライセンス・価格情報・注文方法はこちら	349	380	英語

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サマリー

自動機械学習の市場は、2023年の10億米ドルから2028年には64億米ドルまで、予測期間中に44.6%のCAGRで成長すると予測されます。説明可能なAIは、機械学習モデルがどのように予測を行うかについて透明性を提供することを目的とした、AutoMLの重要な側面です。特徴の重要度や決定木などの説明可能なAI技術を使用することで、企業はモデルがどのように機能するかについての洞察を得ることができ、より多くの情報に基づいた意思決定を行うことができます。
予測期間中、BFSIバーティカルが最大の市場になると予測される
AutoMLは、反復的で時間のかかる作業を自動化し、生産性、効率性、高いスケールで機械学習モデルを構築し、機械学習モデルの実装と訓練に必要な知識ベースのリソースを最小限に抑えるために、BFSI分野で使用されている新しい技術です。AutoMLは、クレジットカードの不正利用検知、リスク評価、投資のリアルタイム損益予測などに活用することができます。また、AutoMLは、データ抽出やアルゴリズムを自動化し、分析の手作業部分をなくし、導入時間を大幅に短縮することができます。例えば、コンセンサス・コーポレーションは、AutoMLを使用することで、導入時間を3～4週間から8時間まで短縮しました。AutoMLは、BFSIセクターにおけるエラーやバイアスの可能性を最小限に抑えることで、企業がインサイトを高め、モデルの精度を向上させることを支援します。AutoMLは、BFSI業界にいくつかのメリットを提供します。複雑で時間のかかる手作業のデータサイエンス・プロセスの必要性を低減し、データサイエンティストの作業を加速させることができるのです。また、AutoMLはデータによるビジネスパフォーマンスの最適化を支援し、ビジネスリーダーがリアルタイムの分析で意思決定を行うことを可能にします。
アプリケーションのうち、モデルアンサンブル分野が予測期間中に最も高いCAGRで成長すると予測されています。
モデルアンサンブルのためのAutoMLは、予測精度を向上させるために組み合わせることができるモデルのコレクションを作成するために自動化された技術を使用することを含みます。アンサンブルは、機械学習における一般的な手法で、複数のモデルの予測を組み合わせて、より正確な最終予測を生成するものです。AutoMLでは、バギング、ブースティング、スタッキングなど、様々な手法でモデルのアンサンブルを行うことができます。AutoMLは、異なるアルゴリズムやハイパーパラメーターを用いて複数のモデルを自動的に作成し、アンサンブル技術を使ってそれらを組み合わせることができます。これにより、オーバーフィッティングのリスクを低減し、異なるアルゴリズムの長所を活用できるため、最終モデルの頑健性と精度を向上させることができます。AutoMLをモデルアンサンブルに使用するメリットは、モデルの選択と結合のプロセスを自動化できるため、データサイエンティストの時間と労力を節約できることです。また、AutoMLは、さまざまなアンサンブル手法の性能を評価し、与えられたデータセットで最も優れた性能を発揮するものを選択することができます。
サービスの中では、コンサルティングサービス分野が予測期間中に最大の市場規模を占めると予想されています。
コンサルティングサービスは、一般的にサードパーティベンダーやコンサルティング会社によって提供され、機械学習の戦略や実装に関する専門知識やガイダンスを提供します。コンサルティングサービスは、組織がデータの準備状況を評価し、ユースケースを特定し、組織内で機械学習を実装するためのロードマップを作成するのに役立ちます。AutoMLのコンサルティングサービスは、機械学習ツールやプラットフォームの複雑な状況を把握し、特定のニーズや目標に基づき、どのツールやテクノロジーを使用するかについて、十分な情報に基づいた意思決定を行うことを支援します。また、データの準備、モデルの選択、ハイパーパラメータのチューニングを指導し、機械学習モデルのパフォーマンスと有効性を評価することも可能です。コンサルタントは、オンサイトまたはリモートで作業し、機械学習のライフサイクルを通じて継続的なサポートとガイダンスを提供することができます。専門知識、ガイダンス、教育を提供することで、コンサルタントは、企業が十分な情報に基づいた意思決定を行い、機械学習イニシアチブでより良い結果を得るのを支援することができます。
予測期間中、北米が最大の市場規模を占める
北米は、自動機械学習市場で最大のシェアを占めると推定されています。自動機械学習の世界市場は、北米が支配しています。北米は、世界の自動機械学習市場において最も高い収益を生み出している地域であり、米国が最も高い市場シェアを構成し、カナダがそれに続いています。この地域は、医療、金融、小売など様々な業界で機械学習や人工知能技術の導入率が高く、これがAutoMLソリューションの需要を促進すると予想されます。さらに、この地域にはデータ駆動型のスタートアップ企業や企業が数多く存在することが、北米におけるAutoML市場の成長をさらに後押ししています。
プライマリーの内訳
自動機械学習市場で活動する様々な主要組織の最高経営責任者（CEO）、イノベーション・技術責任者、システムインテグレーター、経営幹部に対して詳細なインタビューを実施したものです。
 企業別：ティアI：35％、ティアII：45％、ティアIII：20
 By Designation：C-レベルエグゼクティブ：35%, 取締役：25%, そしてその他：40%
 地域別APAC30％、欧州：20％、北米：40％、MEA：5％、中南米：5
Automted Machine Learningのソリューションやサービスを世界中で提供している主要ベンダーは、IBM（米国）、Oracle（米国）、Microsoft（米国）、ServiceNow（米国）、Google（米国）、Baidu（中国）、AWS（米国）、Alteryx（米国）、Salesforce（米国）、Altair（米国）、Teradata（米国）、H2O．ai（米）、DataRobot（米）、BigML（米）、Databricks（米）、Dataiku（仏）、Alibaba Cloud（中）、Appier（台湾）、Squark（米）、Aible（米）、Datafold（米）、Boost．ai（ノルウェー）、Tazi.ai（米国）、Akkio（米国）、Valohai（フィンランド）、dotData（米国）、Qlik（米国）、Mathworks（米国）、HPE（米国）、SparkCognition（米国）です。
調査対象範囲
この市場調査は、セグメントにわたる自動機械学習を対象としています。提供、アプリケーション、垂直、地域などの異なるセグメントにおける市場規模および成長可能性の推定を目的としています。市場の主要企業の詳細な競合分析、会社概要、製品や事業の提供、最近の開発、主要な市場戦略に関連する主要な観察も含まれています。
レポート購入の主なメリット
本レポートは、自動機械学習の市場全体とそのサブセグメントにおける収益数の最も近い近似値に関する情報を、本市場の市場リーダー/新規参入者に提供することになるでしょう。本レポートは、ステークホルダーが競争環境を理解し、自社のビジネスを位置づけ、適切な市場参入戦略を計画するために、より良い洞察を得るのに役立つだろう。また、関係者が市場の鼓動を理解するのに役立ち、主要な市場促進要因、阻害要因、課題、および機会に関する情報を提供します。
本レポートは、以下のポイントに関する洞察を提供します：

- 主要な推進要因（AutoMLによる顧客満足度の向上とパーソナライズされた製品推奨への需要の高まり、正確な不正検出へのニーズの高まり、データ量と複雑性の増大、AutoMLを使用したインテリジェントな自動化によるビジネス変革へのニーズの高まり）、阻害要因（機械学習ツールの導入が遅れている、標準化と規制の欠如）についての分析、機会（AI対応ソリューションの需要拡大への対応、補完的技術との統合、意思決定の迅速化とコスト削減の機会の獲得）、課題（熟練人材の不足、AutoMLモデルの解釈と説明の難しさ、AutoMLにおけるデータプライバシー）が自動機械学習市場の成長に影響を与えている。

- 製品開発/イノベーション：自動機械学習市場における今後の技術、研究開発活動、新製品・サービス発表に関する詳細なインサイトを掲載しています。

- 市場開発：有利な市場に関する包括的な情報 - 当レポートでは、さまざまな地域の機械学習自動化市場を分析しています。

- 市場の多様化：新製品・サービス、未開拓の地域、最近の開発、自動機械学習市場戦略への投資に関する詳細な情報。また、自動機械学習市場の鼓動を理解し、主要な市場促進要因、阻害要因、課題、機会に関する情報を提供することで、ステークホルダーに役立ちます。

- 競争力のある評価：自動機械学習市場におけるIBM（米国）、Google（米国）、AWS（米国）、Microsoft（米国）、Salesforce（米国）などの主要プレイヤーの市場シェア、成長戦略、サービス内容を詳細に評価します。

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1 INTRODUCTION 32
1.1 STUDY OBJECTIVES 32
1.2 MARKET DEFINITION 32
1.2.1 INCLUSIONS AND EXCLUSIONS 33
1.3 MARKET SCOPE 33
1.3.1 MARKET SEGMENTATION 34
1.3.2 REGIONS COVERED 34
1.4 YEARS CONSIDERED 35
1.5 CURRENCY CONSIDERED 35
TABLE 1 USD EXCHANGE RATES, 2020–2022 35
1.6 STAKEHOLDERS 36
2 RESEARCH METHODOLOGY 37
2.1 RESEARCH DATA 37
FIGURE 1 AUTOMATED MACHINE LEARNING MARKET: RESEARCH DESIGN 37
2.1.1 SECONDARY DATA 38
2.1.1.1 Key data from secondary sources 38
2.1.2 PRIMARY DATA 39
2.1.2.1 Key data from primary sources 39
2.1.2.2 Key primary interview participants 40
2.1.2.3 Breakup of primary profiles 40
2.1.2.4 Key industry insights 41
2.2 DATA TRIANGULATION 41
2.3 MARKET SIZE ESTIMATION 42
FIGURE 2 AUTOMATED MACHINE LEARNING MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES 42
2.3.1 TOP-DOWN APPROACH 42
2.3.2 BOTTOM-UP APPROACH 43
FIGURE 3 APPROACH 1 (SUPPLY SIDE): REVENUE FROM OFFERINGS OF AUTOMATED MACHINE LEARNING MARKET PLAYERS 43
FIGURE 4 APPROACH 2 – BOTTOM-UP (SUPPLY SIDE): COLLECTIVE REVENUE FROM OFFERINGS OF AUTOMATED MACHINE LEARNING MARKET PLAYERS 44
FIGURE 5 APPROACH 3 – BOTTOM-UP (SUPPLY SIDE): REVENUE AND SUBSEQUENT MARKET ESTIMATION FROM AUTOMATED MACHINE LEARNING MARKET OFFERINGS 44
FIGURE 6 APPROACH 4 – BOTTOM-UP (DEMAND SIDE): SHARE OF AUTOMATED MACHINE LEARNING MARKET OFFERINGS THROUGH OVERALL AUTOMATED MACHINE LEARNING SPENDING 45

2.4 MARKET FORECAST 46
TABLE 2 FACTOR ANALYSIS 46
2.5 RESEARCH ASSUMPTIONS 47
2.6 LIMITATIONS AND RISK ASSESSMENT 48
2.7 IMPACT OF RECESSION ON GLOBAL AUTOMATED MACHINE LEARNING MARKET 49
TABLE 3 IMPACT OF RECESSION ON GLOBAL AUTOMATED MACHINE LEARNING MARKET 49
3 EXECUTIVE SUMMARY 51
TABLE 4 GLOBAL AUTOMATED MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2017–2022 (USD MILLION, Y-O-Y%) 52
TABLE 5 GLOBAL AUTOMATED MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2023–2028 (USD MILLION, Y-O-Y%) 52
FIGURE 7 SOLUTIONS SEGMENT TO LEAD MARKET IN 2023 52
FIGURE 8 PLATFORMS SEGMENT TO ACCOUNT FOR LARGEST SHARE IN 2023 53
FIGURE 9 OM-PREMISES SEGMENT TO ACCOUNT FOR LARGER SHARE DURING FORECAST PERIOD 53
FIGURE 10 CONSULTING SERVICES SEGMENT TO ACCOUNT FOR LARGEST SHARE IN 2023 53
FIGURE 11 DATA PROCESSING SEGMENT TO ACCOUNT FOR LARGEST SHARE IN 2023 54
FIGURE 12 BFSI SEGMENT TO LEAD MARKET IN 2023 54
FIGURE 13 NORTH AMERICA TO ACCOUNT FOR LARGEST SHARE IN 2023 55
4 PREMIUM INSIGHTS 56
4.1 ATTRACTIVE MARKET OPPORTUNITIES FOR PLAYERS IN AUTOMATED MACHINE LEARNING MARKET 56
FIGURE 14 RISING DEMAND FOR PLATFORMS TO TRANSFER DATA FROM ON-PREMISES TO CLOUD TO DRIVE AUTOMATED MACHINE LEARNING MARKET 56
4.2 AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL 57
FIGURE 15 RETAIL & ECOMMERCE SEGMENT TO ACCOUNT FOR LARGEST SHARE DURING FORECAST PERIOD 57
4.3 AUTOMATED MACHINE LEARNING MARKET, BY REGION 57
FIGURE 16 NORTH AMERICA TO ACCOUNT FOR LARGEST SHARE BY 2028 57
4.4 AUTOMATED MACHINE LEARNING MARKET, BY OFFERING AND KEY VERTICAL 58
FIGURE 17 SOLUTIONS AND BFSI SEGMENTS TO ACCOUNT FOR SIGNIFICANT SHARE BY 2028 58
5 MARKET OVERVIEW AND INDUSTRY TRENDS 59
5.1 INTRODUCTION 59
5.2 MARKET DYNAMICS 59
FIGURE 18 AUTOMATED MACHINE LEARNING MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES 59
5.2.1 DRIVERS 60
5.2.1.1 Growing demand for improved customer satisfaction and personalized product recommendations through AutoML 60
5.2.1.2 Increasing need for accurate fraud detection 60
5.2.1.3 Growing data volume and complexity 60
5.2.1.4 Rising need to transform businesses with intelligent automation using AutoML 61
5.2.2 RESTRAINTS 61
5.2.2.1 Slow adoption of machine learning tools 61
5.2.2.2 Lack of standardization and regulations 62
5.2.3 OPPORTUNITIES 62
5.2.3.1 Growing demand for AI-enabled solutions across industries 62
5.2.3.2 Seamless integration between technologies 62
5.2.3.3 Increased accessibility of machine learning solutions 63
5.2.4 CHALLENGES 63
5.2.4.1 Growing shortage of skilled workforce 63
5.2.4.2 Difficulty in interpreting and explaining AutoML models 64
5.2.4.3 Rising threat to data privacy 64
5.3 CASE STUDY ANALYSIS 64
5.3.1 REAL ESTATE 65
5.3.1.1 Case Study 1: Ascendas Singbridge Group improved real estate decision-making by leveraging DataRobot’s AutoML platform 65
5.3.1.2 Case Study 2: G5 employed H2O.AI’s driverless AI platform to address challenges in identifying productive leads 65
5.3.2 BFSI 66
5.3.2.1 Case Study 1: Robotica helped Avant automate key processes and streamline lending operations 66
5.3.2.2 Case Study 2: Domestic and General partnered with DataRobot to improve customer service capabilities 66
5.3.2.3 Case Study 3: H2O.AI’s machine learning platform enabled PayPal to strengthen fraud detection capabilities 67
5.3.3 RETAIL & ECOMMERCE 67
5.3.3.1 Case Study 1: California Design Den partnered with Google Cloud Platform to implement machine learning solutions 67
5.3.4 IT/ITES 68
5.3.4.1 Case Study 1: Contentree helped Consensus simplify data wrangling process and make it efficient 68
5.3.4.2 Case Study 2: DataRobot’s automated machine learning platform helped Demyst automate data science processes 68
5.3.5 HEALTHCARE & LIFESCIENCES 69
5.3.5.1 Case Study 1: DataRobot helped Evariant automate patient risk stratification and readmission prediction 69
5.3.6 MEDIA & ENTERTAINMENT 69
5.3.6.1 Case Study 1: Meredith Corporation worked with Google Cloud to build data analytics platform to handle large volumes of data 69
5.3.7 TRANSPORTATION & LOGISTICS 70
5.3.7.1 Case Study 1: DMWay enabled PGL to integrate and analyze data from multiple sources 70

5.3.8 ENERGY & UTILITIES 70
5.3.8.1 Case Study 1: SparkCognition helped oil & gas industry to build predictive models by leveraging automated machine learning solutions 70
5.4 ECOSYSTEM ANALYSIS 71
FIGURE 19 ECOSYSTEM ANALYSIS 71
TABLE 6 AUTOMATED MACHINE LEARNING MARKET: PLATFORM PROVIDERS 71
TABLE 7 AUTOMATED MACHINE LEARNING MARKET: SERVICE PROVIDERS 72
TABLE 8 AUTOMATED MACHINE LEARNING MARKET: TECHNOLOGY PROVIDERS 73
TABLE 9 AUTOMATED MACHINE LEARNING MARKET: REGULATORY BODIES 73
5.5 HISTORY OF AUTOMATED MACHINE LEARNING 74
5.6 AUTOMATED MACHINE LEARNING PIPELINE FRAMEWORK 75
FIGURE 20 AUTOMATED MACHINE LEARNING PIPELINE FRAMEWORK 75
TABLE 10 AUTOMATED MACHINE LEARNING PIPELINE FRAMEWORK 76
5.7 VALUE CHAIN ANALYSIS 77
FIGURE 21 VALUE CHAIN ANALYSIS 77
5.7.1 DATA COLLECTION & PREPARATION 77
5.7.2 ALGORITHM DEVELOPMENT 78
5.7.3 MODEL TRAINING 78
5.7.4 MODEL TESTING AND VALIDATION 78
5.7.5 DEPLOYMENT AND INTEGRATION 78
5.7.6 MAINTENANCE AND SUPPORT 79
5.8 PRICING MODEL ANALYSIS 79
TABLE 11 AUTOMATED MACHINE LEARNING MARKET: PRICING LEVELS 79
5.9 PATENT ANALYSIS 81
5.9.1 METHODOLOGY 81
5.9.2 DOCUMENT TYPE 81
TABLE 12 PATENTS FILED, 2018–2021 81
5.9.3 INNOVATION AND PATENT APPLICATIONS 81
FIGURE 22 TOTAL NUMBER OF PATENTS GRANTED, 2021–2023 82
5.9.3.1 Top applicants 82
FIGURE 23 TOP TEN COMPANIES WITH HIGHEST NUMBER OF PATENT APPLICATIONS, 2018–2021 82
TABLE 13 TOP 20 PATENT OWNERS, 2018–2021 83
TABLE 14 LIST OF PATENTS IN AUTOMATED MACHINE LEARNING MARKET, 2021–2023 84
5.10 AUTOMATED MACHINE LEARNING TECHNIQUES 84
5.10.1 BAYESIAN OPTIMIZATION 84
5.10.2 REINFORCEMENT LEARNING 85
5.10.3 EVOLUTIONARY ALGORITHM 85
5.10.4 GRADIENT APPROACHES 85
5.11 COMPARISON OF AUTOAI AND AUTOML SOLUTIONS 86
TABLE 15 COMPARISON BETWEEN AUTOAI AND AUTOML SOLUTIONS 86

5.12 BUSINESS MODELS OF AUTOML 86
5.12.1 API MODELS 86
5.12.2 AS-A-SERVICE MODEL 87
5.12.3 CLOUD MODEL 87
5.13 TECHNOLOGY ANALYSIS 88
5.13.1 RELATED TECHNOLOGIES 88
5.13.1.1 Supervised learning 88
5.13.1.2 Unsupervised learning 88
5.13.1.3 Natural language processing 88
5.13.1.4 Computer vision 89
5.13.1.5 Transfer learning 89
5.13.2 ALLIED TECHNOLOGIES 90
5.13.2.1 Cloud computing 90
5.13.2.2 Robotics 90
5.13.2.3 Federated learning 90
5.14 PORTER’S FIVE FORCES ANALYSIS 91
FIGURE 24 PORTER’S FIVE FORCES ANALYSIS 91
TABLE 16 PORTER’S FIVE FORCES ANALYSIS 91
5.14.1 THREAT FROM NEW ENTRANTS 92
5.14.2 THREAT FROM SUBSTITUTES 92
5.14.3 BARGAINING POWER OF SUPPLIERS 92
5.14.4 BARGAINING POWER OF BUYERS 92
5.14.5 INTENSITY OF COMPETITIVE RIVALRY 92
5.15 KEY CONFERENCES & EVENTS 93
TABLE 17 DETAILED LIST OF CONFERENCES & EVENTS, 2023–2024 93
5.16 REGULATORY LANDSCAPE 94
5.16.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 94
TABLE 18 NORTH AMERICA: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 94
TABLE 19 EUROPE: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 95
TABLE 20 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 97
TABLE 21 ROW: REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 98
5.16.1.1 North America 98
5.16.1.1.1 US 98
5.16.1.1.2 Canada 98
5.16.1.2 Europe 98
5.16.1.3 Asia Pacific 99
5.16.1.3.1 South Korea 99
5.16.1.3.2 China 99
5.16.1.3.3 India 99
5.16.1.4 Middle East & Africa 99
5.16.1.4.1 UAE 99
5.16.1.4.2 KSA 99
5.16.1.4.3 Bahrain 99
5.16.1.5 Latin America 99
5.16.1.5.1 Brazil 100
5.16.1.5.2 Mexico 100
5.17 KEY STAKEHOLDERS & BUYING CRITERIA 100
5.17.1 KEY STAKEHOLDERS IN BUYING PROCESS 100
FIGURE 25 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS 100
TABLE 22 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP THREE VERTICALS 100
5.17.2 BUYING CRITERIA 101
FIGURE 26 KEY BUYING CRITERIA FOR TOP THREE VERTICALS 101
TABLE 23 KEY BUYING CRITERIA FOR TOP THREE VERTICALS 101
5.18 BEST PRACTICES IN AUTOMATED MACHINE LEARNING MARKET 101
5.19 DISRUPTIONS IMPACTING BUYERS/CLIENTS IN AUTOMATED MACHINE LEARNING MARKET 102
FIGURE 27 AUTOMATED MACHINE LEARNING MARKET: DISRUPTIONS IMPACTING BUYERS/CLIENTS 102
5.20 FUTURE DIRECTIONS OF AUTOMATED MACHINE LEARNING LANDSCAPE 103
TABLE 24 SHORT-TERM ROADMAP, 2023–2025 103
TABLE 25 MID-TERM ROADMAP, 2026–2028 103
TABLE 26 LONG-TERM ROADMAP, 2029–2030 104
6 AUTOMATED MACHINE LEARNING MARKET, BY OFFERING 106
6.1 INTRODUCTION 107
6.1.1 OFFERINGS: AUTOMATED MACHINE LEARNING MARKET DRIVERS 107
FIGURE 28 SERVICES SEGMENT TO GROW AT HIGHER CAGR DURING FORECAST PERIOD 107
TABLE 27 AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 108
TABLE 28 AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 108
6.2 SOLUTIONS 108
TABLE 29 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 108
TABLE 30 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 109
6.2.1 AUTOMATED MACHINE LEARNING SOLUTIONS, BY TYPE 109
FIGURE 29 PLATFORMS SEGMENT TO WITNESS HIGHER GROWTH DURING FORECAST PERIOD 109
TABLE 31 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 110
TABLE 32 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 110
6.2.1.1 Platforms 110
6.2.1.1.1 Ease of use and deployment to drive adoption of automated machine learning platforms 110
TABLE 33 PLATFORMS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 111
TABLE 34 PLATFORMS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 111
6.2.1.2 Software 111
6.2.1.2.1 Ease of integration into existing machine learning workflows to boost deployment of automated machine learning software solutions 111
TABLE 35 SOFTWARE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 112
TABLE 36 SOFTWARE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 112
6.2.2 AUTOMATED MACHINE LEARNING SOLUTIONS, BY DEPLOYMENT 112
FIGURE 30 ON-PREMISES SEGMENT TO WITNESS HIGHER CAGR DURING FORECAST PERIOD 113
TABLE 37 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 113
TABLE 38 SOLUTIONS: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 113
6.2.2.1 On-premises 114
6.2.2.1.1 Increased control over data and infrastructure to drive on-premises deployment of automated machine learning solutions 114
TABLE 39 ON-PREMISES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 114
TABLE 40 ON-PREMISES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 114
6.2.2.2 Cloud 115
6.2.2.2.1 Flexibility and scalability of cloud-based AutoML solutions to boost market growth 115
TABLE 41 CLOUD: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 115
TABLE 42 CLOUD: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 115
6.3 SERVICES 116
FIGURE 31 TRAINING, SUPPORT, AND MAINTENANCE SEGMENT TO ACCOUNT FOR LARGEST SHARE DURING FORECAST PERIOD 116
TABLE 43 SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 116
TABLE 44 SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 117
TABLE 45 SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 117
TABLE 46 SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 117
6.3.1 CONSULTING SERVICES 118
6.3.1.1 Rising demand for expert guidance on machine learning strategies to drive growth of automated machine learning consulting services 118
TABLE 47 CONSULTING SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 118
TABLE 48 CONSULTING SERVICES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 118
6.3.2 DEPLOYMENT AND INTEGRATION 119
6.3.2.1 Rising demand for integrating machine learning models into existing workflows and applications to boost adoption of AutoML deployment and integration services 119
TABLE 49 DEPLOYMENT AND INTEGRATION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 119
TABLE 50 DEPLOYMENT AND INTEGRATION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 119
6.3.3 TRAINING, SUPPORT, AND MAINTENANCE 120
6.3.3.1 Rising preference for optimal model performance and accuracy to drive use of AutoML training, support, and maintenance services 120
TABLE 51 TRAINING, SUPPORT, AND MAINTENANCE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 120
TABLE 52 TRAINING, SUPPORT, AND MAINTENANCE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 120
7 AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION 121
7.1 INTRODUCTION 122
7.1.1 APPLICATIONS: AUTOMATED MACHINE LEARNING MARKET DRIVERS 122
FIGURE 32 DATA PROCESSING SEGMENT TO LEAD MARKET DURING FORECAST PERIOD 123
TABLE 53 AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 123
TABLE 54 AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 124
7.2 DATA PROCESSING 124
7.2.1 GROWING NEED TO DETECT AND CORRECT DATA ERRORS TO DRIVE ADOPTION OF AUTOML SOLUTIONS FOR DATA PROCESSING 124
TABLE 55 DATA PROCESSING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 124
TABLE 56 DATA PROCESSING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 125
7.2.2 CLEANING 125
7.2.3 TRANSFORMATION 125
7.2.4 VISUALIZATION 125
7.3 MODEL SELECTION 126
7.3.1 RISING DEMAND FOR AUTOMATED TECHNIQUES TO HANDLE COMPLEX DATA TO BOOST GROWTH OF AUTOML SOLUTIONS FOR MODEL SELECTION 126
TABLE 57 MODEL SELECTION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 126
TABLE 58 MODEL SELECTION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 127
7.3.2 SCALING 127
7.3.3 MONITORING 127
7.3.4 VERSIONING 128
7.4 HYPERPARAMETER OPTIMIZATION & TUNING 128
7.4.1 INCREASED ADOPTION OF AUTOML ALGORITHMS FOR HYPERPARAMETER OPTIMIZATION TO DRIVE MARKET GROWTH 128
TABLE 59 HYPERPARAMETER TUNING & OPTIMIZATION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 128
TABLE 60 HYPERPARAMETER TUNING & OPTIMIZATION: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 129
7.4.2 GRID SEARCH 129
7.4.3 RANDOM SEARCH 129
7.4.4 BAYESIAN SEARCH 130
7.5 FEATURE ENGINEERING 130
7.5.1 RISING NEED TO TRANSFORM RAW DATA INTO SET OF FEATURES FOR USE IN MACHINE LEARNING MODELS TO BOOST ADOPTION OF AUTOML SOLUTIONS IN FEATURE ENGINEERING 130
TABLE 61 FEATURE ENGINEERING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 130
TABLE 62 FEATURE ENGINEERING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 131
7.6 MODEL ENSEMBLING 132
7.6.1 GROWING IMPORTANCE OF IMPROVING PREDICTION ACCURACY TO PROPEL GROWTH OF AUTOML SOLUTIONS FOR MODEL ENSEMBLING 132
7.6.2 INFRASTRUCTURE & FORMAT 133
7.6.3 INTEGRATION 133
7.6.4 MAINTENANCE 134
7.7 OTHER APPLICATIONS 134
TABLE 65 OTHER APPLICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 134
TABLE 66 OTHER APPLICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 135
8 AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL 136
8.1 INTRODUCTION 137
8.1.1 VERTICALS: AUTOMATED MACHINE LEARNING MARKET DRIVERS 137
FIGURE 33 BFSI SEGMENT TO ACCOUNT FOR LARGER MARKET SIZE DURING FORECAST PERIOD 137
TABLE 67 AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 138
TABLE 68 AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 138

8.2 BANKING, FINANCIAL SERVICES, AND INSURANCE 139
8.2.1 NEED TO OPTIMIZE BUSINESS PERFORMANCE WITH REAL-TIME ANALYTICS TO DRIVE USE OF AUTOML SOLUTIONS IN BFSI SECTOR 139
TABLE 69 BFSI: USE CASES 139
TABLE 70 BFSI: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 140
TABLE 71 BFSI: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 140
TABLE 72 BFSI: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 140
TABLE 73 BFSI: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 141
8.2.2 CREDIT SCORING 141
8.2.3 FRAUD DETECTION 141
8.2.4 RISK ANALYSIS & MANAGEMENT 142
8.2.5 OTHER BFSI SUB-VERTICALS 142
8.3 HEALTHCARE & LIFE SCIENCES 142
8.3.1 DEMAND FOR IMPROVED DIAGNOSES AND PERSONALIZED TREATMENT PLANS TO DRIVE MARKET FOR AI AND ML SOLUTIONS FOR HEALTHCARE & LIFE SCIENCES INDUSTRY 142
TABLE 74 HEALTHCARE & LIFESCIENCES: USE CASES 143
TABLE 75 HEALTHCARE & LIFE SCIENCES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 143
TABLE 76 HEALTHCARE & LIFE SCIENCES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 144
TABLE 77 HEALTHCARE & LIFE SCIENCES: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 144
TABLE 78 HEALTHCARE & LIFE SCIENCES: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 144
8.3.2 ANOMALY DETECTION 145
8.3.3 DISEASE DIAGNOSIS 145
8.3.4 DRUG DISCOVERY 145
8.3.5 OTHER HEALTHCARE SUB-VERTICALS 145
8.4 RETAIL & ECOMMERCE 146
8.4.1 GROWING NEED FOR PERSONALIZATION AND OPTIMIZATION IN HIGHLY COMPETITIVE INDUSTRIES TO BOOST MARKET GROWTH 146
TABLE 79 RETAIL & ECOMMERCE: USE CASES 146
TABLE 80 RETAIL & ECOMMERCE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 147
TABLE 81 RETAIL & ECOMMERCE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 147
TABLE 82 RETAIL & ECOMMERCE: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 147
TABLE 83 RETAIL & ECOMMERCE: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 148
8.4.2 DEMAND FORECASTING 148
8.4.3 PRICE OPTIMIZATION 148
8.4.4 RECOMMENDATION ENGINES 148
8.4.5 SENTIMENT ANALYSIS 149
8.4.6 SOCIAL MEDIA ANALYTICS 149
8.4.7 CHATBOTS FOR CUSTOMER SERVICE & SUPPORT 149
8.4.8 OTHER RETAIL & ECOMMERCE SUB-VERTICALS 149
8.5 MANUFACTURING 150
8.5.1 AUTOML SOLUTIONS TO OPTIMIZE MANUFACTURING PROCESS AND IMPROVE EFFICIENCY 150
TABLE 84 MANUFACTURING: USE CASES 150
TABLE 85 MANUFACTURING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 150
TABLE 86 MANUFACTURING: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 151
TABLE 87 MANUFACTURING: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 151
TABLE 88 MANUFACTURING: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 151
8.5.2 PREDICTIVE MAINTENANCE 152
8.5.3 QUALITY CONTROL 152
8.5.4 ROBOTIC PROCESS AUTOMATION 152
8.5.5 SUPPLY CHAIN OPTIMIZATION 152
8.5.6 OTHER MANUFACTURING SUB-VERTICALS 153
8.6 GOVERNMENT & DEFENSE 153
8.6.1 RISING NEED TO EMPOWER NATIONAL SECURITY AND PUBLIC SERVICES TO DRIVE ADOPTION OF AUTOML PLATFORMS IN GOVERNMENT & DEFENSE SECTOR 153
TABLE 89 GOVERNMENT & DEFENSE: USE CASES 153
TABLE 90 GOVERNMENT & DEFENSE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 154
TABLE 91 GOVERNMENT & DEFENSE: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 154
TABLE 92 GOVERNMENT & DEFENSE: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 154
TABLE 93 GOVERNMENT & DEFENSE: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 155
8.6.2 CYBERSECURITY THREAT DETECTION 155
8.6.3 FRAUD DETECTION & PREVENTION 155
8.6.4 NATURAL DISASTER MANAGEMENT 156
8.6.5 CUSTOMER SERVICE CHATBOTS 156
8.6.6 OTHER GOVERNMENT & DEFENSE SUB-VERTICALS 156
8.7 TELECOMMUNICATIONS 157
8.7.1 NEED FOR ENHANCED CUSTOMER SERVICE TO BOOST USE OF AUTOML SOLUTIONS IN TELECOMMUNICATIONS INDUSTRY 157
TABLE 94 TELECOMMUNICATIONS: USE CASES 157
TABLE 95 TELECOMMUNICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 158
TABLE 96 TELECOMMUNICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 158
TABLE 97 TELECOMMUNICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 158
TABLE 98 TELECOMMUNICATIONS: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 159
8.7.2 CYBERSECURITY THREAT DETECTION 159
8.7.3 NETWORK OPTIMIZATION 159
8.7.4 PREDICTIVE MAINTENANCE 160
8.7.5 FRAUD DETECTION & PREVENTION 160
8.7.6 CHATBOTS & VIRTUAL ASSISTANCE 160
8.7.7 OTHER TELECOMMUNICATIONS SUB-VERTICALS 160
8.8 IT/ITES 161
8.8.1 NEED TO OPTIMIZE PROCESSES AND ENHANCE CYBERSECURITY TO PROPEL GROWTH OF AUTOMATED MACHINE LEARNING MARKET FOR IT/ITES SECTOR 161
TABLE 99 IT/ITES: USE CASES 161
TABLE 100 IT/ITES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 162
TABLE 101 IT/ITES: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 162
TABLE 102 IT/ITES: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 162
TABLE 103 IT/ITES: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 163
8.8.2 PREDICTIVE MAINTENANCE 163
8.8.3 VIRTUAL ASSISTANTS FOR CUSTOMER SUPPORT 163
8.8.4 NETWORK OPTIMIZATION 163
8.8.5 OTHER IT/ITES SUB-VERTICALS 164
8.9 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS 164
8.9.1 AUTOMATED MACHINE LEARNING SOLUTIONS TO ENABLE ORGANIZATIONS TO LEVERAGE DATA AND GAIN INSIGHTS FOR BETTER BUSINESS DECISIONS 164
TABLE 104 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS: USE CASES 165
TABLE 105 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 165
TABLE 106 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 166
TABLE 107 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 166
TABLE 108 AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 166
8.9.2 AUTONOMOUS VEHICLES 167
8.9.3 ROUTE OPTIMIZATION 167
8.9.4 FUEL EFFICIENCY PREDICTION & OPTIMIZATION 167
8.9.5 HUMAN MACHINE INTERFACE (HMI) 167
8.9.6 SEMI-AUTONOMOUS DRIVING 167
8.9.7 ROBOTIC PROCESS AUTOMATION 167
8.9.8 OTHER AUTOMOTIVE, TRANSPORTATION, AND LOGISTICS SUB-VERTICALS 168
8.10 MEDIA & ENTERTAINMENT 168
8.10.1 USE OF AUTOML SOLUTIONS TO ENSURE IMPROVED CONTENT DISCOVERY 168
TABLE 109 MEDIA & ENTERTAINMENT: USE CASES 169
TABLE 110 MEDIA & ENTERTAINMENT: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 169
TABLE 111 MEDIA & ENTERTAINMENT: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 170
TABLE 112 MEDIA & ENTERTAINMENT: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2017–2022 (USD MILLION) 170
TABLE 113 MEDIA & ENTERTAINMENT: AUTOMATED MACHINE LEARNING MARKET, BY SUB-VERTICAL, 2023–2028 (USD MILLION) 170
8.10.2 IMAGE & SPEECH RECOGNITION 171
8.10.3 RECOMMENDATION SYSTEMS 171
8.10.4 SENTIMENT ANALYSIS 171
8.10.5 OTHER MEDIA & ENTERTAINMENT SUB-VERTICALS 171
8.11 OTHER VERTICALS 172
TABLE 114 OTHER VERTICALS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 172
TABLE 115 OTHER VERTICALS: AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 172
9 AUTOMATED MACHINE LEARNING MARKET, BY REGION 173
9.1 INTRODUCTION 174
FIGURE 34 ASIA PACIFIC TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 174
FIGURE 35 INDIA TO GROW AT HIGHEST CAGR DURING FORECAST PERIOD 175
TABLE 116 AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2017–2022 (USD MILLION) 175
TABLE 117 AUTOMATED MACHINE LEARNING MARKET, BY REGION, 2023–2028 (USD MILLION) 175
9.2 NORTH AMERICA 176
9.2.1 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET DRIVERS 176
9.2.2 NORTH AMERICA: RECESSION IMPACT 176
FIGURE 36 NORTH AMERICA: MARKET SNAPSHOT 177
TABLE 118 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 177
TABLE 119 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 178
TABLE 120 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 178
TABLE 121 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 178
TABLE 122 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 178
TABLE 123 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 179
TABLE 124 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 179
TABLE 125 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 179
TABLE 126 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 180
TABLE 127 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 180
TABLE 128 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 181
TABLE 129 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 181
TABLE 130 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2017–2022 (USD MILLION) 182
TABLE 131 NORTH AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2023–2028 (USD MILLION) 182
9.2.3 US 182
9.2.3.1 Growing demand for efficient ways to build and deploy machine learning models to drive market growth 182
TABLE 132 US: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 183
TABLE 133 US: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 183
TABLE 134 US: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 183
TABLE 135 US: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 183
TABLE 136 US: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 184
TABLE 137 US: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 184
TABLE 138 US: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 184
TABLE 139 US: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 184
9.2.4 CANADA 185
9.2.4.1 Rising adoption of machine learning applications in various industries across Canada to fuel market growth 185
9.3 EUROPE 185
9.3.1 EUROPE: AUTOMATED MACHINE LEARNING MARKET DRIVERS 185
9.3.2 EUROPE: RECESSION IMPACT 186
TABLE 140 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 186
TABLE 141 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 187
TABLE 142 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 187
TABLE 143 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 187
TABLE 144 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 187
TABLE 145 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 188
TABLE 146 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 188
TABLE 147 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 188
TABLE 148 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 189
TABLE 149 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 189
TABLE 150 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 190
TABLE 151 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 190
TABLE 152 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2017–2022 (USD MILLION) 191
TABLE 153 EUROPE: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2023–2028 (USD MILLION) 191
TABLE 154 UK: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 192
TABLE 155 UK: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 192
TABLE 156 UK: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 192
TABLE 157 UK: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 192
TABLE 158 UK: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 193
TABLE 159 UK: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 193
TABLE 160 UK: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 193
TABLE 161 UK: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 193
9.3.4 GERMANY 194
9.3.4.1 Strong IT infrastructure and robust regulatory framework to drive AutoML market in Germany 194
9.3.5 FRANCE 194
9.3.5.1 Country’s thriving startup ecosystem to boost adoption of automated machine learning solutions 194
9.3.6 ITALY 195
9.3.6.1 Significant initiatives taken by government to promote use of automated machine learning platforms to boost market growth 195
9.3.7 SPAIN 195
9.3.7.1 Rising technological investments by major players to boost popularity of AutoML platforms and solutions in Spain 195
9.3.8 NORDIC 196
9.3.8.1 Increasing research and development in AI and machine learning in Nordic countries to drive market growth 196
9.3.9 REST OF EUROPE 196

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Table of Contents

Summary

The market for Automated Machine Learning is projected to grow from USD 1.0 billion in 2023 to USD 6.4 billion by 2028, at a CAGR of 44.6% during the forecast period. Explainable AI is a crucial aspect of AutoML that aims to provide transparency into how machine learning models make predictions. By using explainable AI techniques, such as feature importance and decision trees, businesses can gain insights into how their models work and make more informed decisions.
The BFSI vertical is projected to be the largest market during the forecast period
AutoML is an emerging technology used in the BFSI sectors to automate iterative and time-consuming tasks, build machine learning models with productivity, efficiency, and high scale, and minimize the knowledge-based resources needed to implement and train machine learning models. AutoML can be used for credit card fraud detection, risk assessment, and real-time gain and loss prediction for investments. AutoML can also help reduce deployment time by automating data extraction and algorithms, eliminating manual parts of the analyses, and significantly reducing deployment time. For instance, the Consensus Corporation reduced its deployment time from 3-4 weeks to eight hours using AutoML. AutoML can help enterprises boost insights and enhance model accuracy by minimizing the chances of error or bias in the BFSI sector. AutoML provides several benefits to the BFSI industry. It helps to reduce the need for manual data science processes, which can be complex and time-consuming, and can accelerate the work of data scientists. AutoML can also help optimize business performance driven by data, enabling business leaders to make decisions with real-time analytics.
Among Application, model ensembling segment is registered to grow at the highest CAGR during the forecast period
AutoML for model ensembling involves the use of automated techniques to create a collection of models that can be combined to improve prediction accuracy. Ensembling is a popular technique in machine learning that involves combining the predictions of multiple models to generate a more accurate final prediction. AutoML can use various techniques for model ensembling, such as bagging, boosting, and stacking. AutoML can automatically create multiple models using different algorithms and hyperparameters and then combine them using ensembling techniques. This can improve the robustness and accuracy of the final model, as it reduces the risk of overfitting and leverages the strengths of different algorithms. The benefit of using AutoML for model ensembling is that it can automate the process of selecting and combining models, which can save time and effort for data scientists. AutoML can also evaluate the performance of different ensembling methods and select the one that performs the best on the given dataset.
Among services, consulting services segment is anticipated to account for the largest market size during the forecast period
Consulting services are typically offered by third-party vendors or consulting firms, providing expertise and guidance on machine learning strategy and implementation. Consulting services can help organizations evaluate their data readiness, identify use cases, and develop a roadmap for implementing machine learning within their organization. AutoML consulting services can help organizations navigate the complex landscape of machine learning tools and platforms and make informed decisions about which tools and technologies to use based on their specific needs and goals. Consultants can also guide data preparation, model selection, and hyperparameter tuning and can help organizations evaluate the performance and effectiveness of their machine learning models. Consultants may work onsite or remotely and provide ongoing support and guidance throughout the machine learning lifecycle. By providing expertise, guidance, and education, consultants can help organizations make informed decisions and achieve better results with their machine learning initiatives.
North America to account for the largest market size during the forecast period
North America is estimated to account for the largest share of the Automated Machine Learning market. The global market for Automated Machine Learning is dominated by North America. North America is the highest revenue-generating region in the global Automated Machine Learning market, with the US constituting the highest market share, followed by Canada. The region has a high adoption rate of machine learning and artificial intelligence technologies across various industries, including healthcare, finance, and retail, which is expected to drive the demand for AutoML solutions. Moreover, the presence of a large number of data-driven startups and companies in the region is further fueling the growth of the AutoML market in North America.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the Automated Machine Learning market.
 By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
 By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
 By Region: APAC: 30%, Europe: 20%, North America: 40%, MEA: 5%, Latin America: 5%
Major vendors offering Automted Machine Learning solutions and services across the globe are IBM (US), Oracle (US), Microsoft (US), ServiceNow (US), Google (US), Baidu (China), AWS (US), Alteryx (US), Salesforce (US), Altair (US), Teradata (US), H2O.ai (US), DataRobot (US), BigML (US), Databricks (US), Dataiku (France), Alibaba Cloud (China), Appier (Taiwan), Squark (US), Aible (US), Datafold (US), Boost.ai (Norway), Tazi.ai (US), Akkio (US), Valohai (Finland), dotData (US), Qlik (US), Mathworks (US), HPE (US), and SparkCognition (US).
Research Coverage
The market study covers Automated Machine Learning across segments. It aims at estimating the market size and the growth potential across different segments, such as offering, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for Automated Machine Learning and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:

• Analysis of key drivers (Growing demand for improved customer satisfaction and personalized product recommendations through AutoML, Increasing need for accurate fraud detection, Growing data volume and complexity, Rising need to transform businesses with Intelligent automation using AutoML), restraints (Machine learning tools are being slowly adopted, Lack of standardization and regulations), opportunities (Capitalizing on growing demand for AI-enabled solutions, Integration with complementary technologies, Seizing opportunities for faster decision-making and cost savings ), and challenges (Increasing shortage of skilled talent, Difficulty in Interpreting and explaining AutoML models, Data privacy in AutoML) influencing the growth of the Automated Machine Learning market

• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Automated Machine Learning market.

• Market Development: Comprehensive information about lucrative markets – the report analyses the Automated Machine Learning market across varied regions

• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in Automated Machine Learning market strategies; the report also helps stakeholders understand the pulse of the Automated Machine Learning market and provides them with information on key market drivers, restraints, challenges, and opportunities

• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as IBM (US), Google (US), AWS(US), Microsoft (US), Salesforce (US), among others in the Automated Machine Learning market.

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ご注文は、お電話またはWEBから承ります。お見積もりの作成もお気軽にご相談ください。

本レポートと同分野（機械学習）の最新刊レポート

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