出版：MarketsandMarkets（マーケッツアンドマーケッツ） 出版年月：2023年5月
自動機械学習（AutoML）市場 : オファリング (ソリューションとサービス)、用途 (データ処理、モデルの選択、ハイパーパラメーターの最適化とチューニング、特徴量エンジニアリング、モデルのアンサンブル)、業種と地域別 – 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
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| 種別 | 英文調査報告書 |
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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.
目次
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
9.4 ASIA PACIFIC 196
9.4.1 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET DRIVERS 197
9.4.2 ASIA PACIFIC: RECESSION IMPACT 197
FIGURE 37 ASIA PACIFIC: MARKET SNAPSHOT 198
TABLE 162 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 198
TABLE 163 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 199
TABLE 164 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 199
TABLE 165 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 199
TABLE 166 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 199
TABLE 167 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 200
TABLE 168 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 200
TABLE 169 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 200
TABLE 170 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 201
TABLE 171 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 201
TABLE 172 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 202
TABLE 173 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 202
TABLE 174 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2017–2022 (USD MILLION) 203
TABLE 175 ASIA PACIFIC: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2023–2028 (USD MILLION) 203
9.4.3 CHINA 204
9.4.3.1 Heavy investments made in machine learning technology to drive growth of automated machine learning solutions in China 204
TABLE 176 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 204
TABLE 177 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 204
TABLE 178 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 205
TABLE 179 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 205
TABLE 180 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 205
TABLE 181 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 205
TABLE 182 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 206
TABLE 183 CHINA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 206
9.4.4 JAPAN 206
9.4.4.1 Growing need for technological enhancements to boost growth of AutoML solutions and services in Japan 206
9.4.5 SOUTH KOREA 207
9.4.5.1 Strong focus on developing cutting-edge technologies to boost use of AutoML solutions across sectors in South Korea 207
9.4.6 ASEAN 207
9.4.6.1 Rising demand to leverage machine learning solutions for competitive advantage to boost growth of automated machine learning market 207
9.4.7 AUSTRALIA & NEW ZEALAND 207
9.4.7.1 Increased innovations by major companies specializing in machine learning to drive adoption of AutoML solutions across industries 207
9.4.8 REST OF ASIA PACIFIC 208
9.5 MIDDLE EAST & AFRICA 208
9.5.1 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET DRIVERS 208
9.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT 209
TABLE 184 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 209
TABLE 185 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 209
TABLE 186 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 209
TABLE 187 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 210
TABLE 188 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 210
TABLE 189 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 210
TABLE 190 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 210
TABLE 191 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 211
TABLE 192 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 211
TABLE 193 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 211
TABLE 194 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 212
TABLE 195 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 212
TABLE 196 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2017–2022 (USD MILLION) 213
TABLE 197 MIDDLE EAST & AFRICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2023–2028 (USD MILLION) 213
9.5.3 SAUDI ARABIA 213
9.5.3.1 Saudi Arabia’s commitment to leveraging AI and ML technologies to drive market growth 213
9.5.4 UAE 214
9.5.4.1 Rising growth of advanced technologies to drive market for AI and ML solutions and services 214
9.5.5 ISRAEL 214
9.5.5.1 Growing investments in AI and ML research by major players to boost growth of automated machine learning market in Israel 214
9.5.6 TURKEY 215
9.5.6.1 Growing ecosystem and adoption of machine learning technology across industries to boost market growth in Turkey 215
9.5.7 SOUTH AFRICA 215
9.5.7.1 Increasing investments and initiatives from governments and private sector to drive popularity of AI and ML solutions 215
9.5.8 REST OF MIDDLE EAST & AFRICA 216
9.6 LATIN AMERICA 216
9.6.1 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET DRIVERS 216
9.6.2 LATIN AMERICA: RECESSION IMPACT 217
TABLE 198 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2017–2022 (USD MILLION) 217
TABLE 199 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY OFFERING, 2023–2028 (USD MILLION) 217
TABLE 200 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2017–2022 (USD MILLION) 217
TABLE 201 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY TYPE, 2023–2028 (USD MILLION) 218
TABLE 202 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2017–2022 (USD MILLION) 218
TABLE 203 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY DEPLOYMENT, 2023–2028 (USD MILLION) 218
TABLE 204 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2017–2022 (USD MILLION) 218
TABLE 205 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY SERVICE, 2023–2028 (USD MILLION) 219
TABLE 206 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2017–2022 (USD MILLION) 219
TABLE 207 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 219
TABLE 208 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2017–2022 (USD MILLION) 220
TABLE 209 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 220
TABLE 210 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2017–2022 (USD MILLION) 221
TABLE 211 LATIN AMERICA: AUTOMATED MACHINE LEARNING MARKET, BY COUNTRY, 2023–2028 (USD MILLION) 221
9.6.3 BRAZIL 221
9.6.3.1 Significant government support to drive adoption of AI and ML technologies across industries 221
9.6.4 MEXICO 222
9.6.4.1 Rapid growth in country’s technology sector to drive market for automated machine learning 222
9.6.5 ARGENTINA 222
9.6.5.1 Government incentives to foreign companies for investments in country’s technology sector to boost AutoML market growth 222
9.6.6 REST OF LATIN AMERICA 223
10 COMPETITIVE LANDSCAPE 224
10.1 OVERVIEW 224
10.2 STRATEGIES ADOPTED BY KEY PLAYERS 224
TABLE 212 STRATEGIES ADOPTED BY KEY PLAYERS 224
10.3 REVENUE ANALYSIS 225
FIGURE 38 REVENUE ANALYSIS FOR KEY PLAYERS, 2018–2022 225
10.4 MARKET SHARE ANALYSIS 225
FIGURE 39 MARKET SHARE ANALYSIS FOR KEY PLAYERS, 2022 226
TABLE 213 AUTOMATED MACHINE LEARNING MARKET: INTENSITY OF COMPETITIVE RIVALRY 226
10.5 EVALUATION QUADRANT MATRIX FOR KEY PLAYERS 226
10.5.1 STARS 226
10.5.2 EMERGING LEADERS 227
10.5.3 PERVASIVE PLAYERS 227
10.5.4 PARTICIPANTS 227
FIGURE 40 EVALUATION QUADRANT MATRIX FOR KEY PLAYERS, 2023 228
10.6 EVALUATION QUADRANT MATRIX FOR SMES/STARTUPS 229
10.6.1 PROGRESSIVE COMPANIES 229
10.6.2 RESPONSIVE COMPANIES 229
10.6.3 DYNAMIC COMPANIES 229
10.6.4 STARTING BLOCKS 229
FIGURE 41 EVALUATION QUADRANT MATRIX FOR SMES/STARTUPS, 2023 230
10.7 COMPETITIVE BENCHMARKING 231
TABLE 214 COMPETITIVE BENCHMARKING FOR KEY PLAYERS, 2023 231
TABLE 215 DETAILED LIST OF KEY SMES/STARTUPS 232
TABLE 216 COMPETITIVE BENCHMARKING FOR SMES/STARTUPS, 2023 233
10.8 AUTOMATED MACHINE LEARNING PRODUCT LANDSCAPE 234
10.8.1 COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING PRODUCTS 234
TABLE 217 COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING PRODUCTS 234
FIGURE 42 COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING PRODUCTS 235
10.9 COMPETITIVE SCENARIO 235
10.9.1 PRODUCT LAUNCHES 235
TABLE 218 AUTOMATED MACHINE LEARNING MARKET: PRODUCT LAUNCHES, 2020–2023 235
10.9.2 DEALS 236
TABLE 219 AUTOMATED MACHINE LEARNING MARKET: DEALS, 2020–2023 236
10.9.3 OTHERS 238
TABLE 220 AUTOMATED MACHINE LEARNING MARKET: OTHERS, 2020–2022 238
10.10 VALUATION AND FINANCIAL METRICS OF KEY AUTOMATED MACHINE LEARNING VENDORS 238
FIGURE 43 VALUATION AND FINANCIAL METRICS OF KEY AUTOMATED MACHINE LEARNING VENDORS 238
10.11 YTD PRICE TOTAL RETURN AND STOCK BETA OF KEY AUTOMATED MACHINE LEARNING VENDORS 239
FIGURE 44 YTD PRICE TOTAL RETURN AND STOCK BETA OF KEY AUTOMATED MACHINE LEARNING VENDORS 239
11 COMPANY PROFILES 240
11.1 INTRODUCTION 240
11.2 KEY PLAYERS 240
(Business Overview, Products/Solutions offered, Recent Developments, MnM View)*
11.2.1 IBM 240
TABLE 221 IBM: BUSINESS OVERVIEW 240
FIGURE 45 IBM: COMPANY SNAPSHOT 241
TABLE 222 IBM: PRODUCTS/SOLUTIONS OFFERED 241
TABLE 223 IBM: PRODUCT LAUNCHES 243
TABLE 224 IBM: DEALS 244
11.2.2 ORACLE 247
TABLE 225 ORACLE: BUSINESS OVERVIEW 247
FIGURE 46 ORACLE: COMPANY SNAPSHOT 248
TABLE 226 ORACLE: PRODUCTS/SOLUTIONS OFFERED 248
TABLE 227 ORACLE: PRODUCT LAUNCHES 249
TABLE 228 ORACLE: DEALS 250
TABLE 229 ORACLE: OTHERS 251
11.2.3 MICROSOFT 253
TABLE 230 MICROSOFT: BUSINESS OVERVIEW 253
FIGURE 47 MICROSOFT: COMPANY SNAPSHOT 254
TABLE 231 MICROSOFT: PRODUCTS/SOLUTIONS OFFERED 254
TABLE 232 MICROSOFT: PRODUCT LAUNCHES 255
TABLE 233 MICROSOFT: DEALS 256
11.2.4 SERVICENOW 258
TABLE 234 SERVICENOW: BUSINESS OVERVIEW 258
FIGURE 48 SERVICENOW: COMPANY SNAPSHOT 259
TABLE 235 SERVICENOW: PRODUCTS/SOLUTIONS OFFERED 259
TABLE 236 SERVICENOW: PRODUCT LAUNCHES 260
TABLE 237 SERVICENOW: DEALS 261
11.2.5 GOOGLE 264
TABLE 238 GOOGLE: BUSINESS OVERVIEW 264
FIGURE 49 GOOGLE: COMPANY SNAPSHOT 265
TABLE 239 GOOGLE: PRODUCTS/SOLUTIONS OFFERED 265
TABLE 240 GOOGLE: PRODUCT LAUNCHES 267
TABLE 241 GOOGLE: DEALS 269
11.2.6 BAIDU 272
TABLE 242 BAIDU: BUSINESS OVERVIEW 272
FIGURE 50 BAIDU: COMPANY SNAPSHOT 273
TABLE 243 BAIDU: PRODUCTS OFFERED 273
TABLE 244 BAIDU: PRODUCT LAUNCHES 274
TABLE 245 BAIDU: DEALS 275
11.2.7 AWS 276
TABLE 246 AWS: BUSINESS OVERVIEW 276
FIGURE 51 AWS: COMPANY SNAPSHOT 276
TABLE 247 AWS: PRODUCTS/SERVICES OFFERED 277
TABLE 248 AWS: PRODUCT LAUNCHES 278
TABLE 249 AWS: DEALS 280
TABLE 250 AWS: OTHERS 281
11.2.8 ALTERYX 282
TABLE 251 ALTERYX: BUSINESS OVERVIEW 282
FIGURE 52 ALTERYX: COMPANY SNAPSHOT 283
TABLE 252 ALTERYX: PRODUCTS OFFERED 283
TABLE 253 ALTERYX: PRODUCT LAUNCHES 284
TABLE 254 ALTERYX: DEALS 285
11.2.9 HPE 286
TABLE 255 HPE: BUSINESS OVERVIEW 286
FIGURE 53 HPE: COMPANY SNAPSHOT 287
TABLE 256 HPE: PRODUCTS/SOLUTIONS OFFERED 287
TABLE 257 HPE: PRODUCT LAUNCHES 288
TABLE 258 HPE: DEALS 289
11.2.10 SALESFORCE 290
TABLE 259 SALESFORCE: BUSINESS OVERVIEW 290
FIGURE 54 SALESFORCE: COMPANY SNAPSHOT 291
TABLE 260 SALESFORCE: PRODUCTS/SOLUTIONS OFFERED 291
TABLE 261 SALESFORCE: PRODUCT LAUNCHES 292
TABLE 262 SALESFORCE: DEALS 292
11.2.11 ALTAIR 293
TABLE 263 ALTAIR: BUSINESS OVERVIEW 293
FIGURE 55 ALTAIR: COMPANY SNAPSHOT 294
TABLE 264 ALTAIR: PRODUCTS/SOLUTIONS OFFERED 294
TABLE 265 ALTAIR: PRODUCT LAUNCHES 295
TABLE 266 ALTAIR: DEALS 295
11.2.12 TERADATA 296
TABLE 267 TERADATA: BUSINESS OVERVIEW 296
FIGURE 56 TERADATA: COMPANY SNAPSHOT 297
TABLE 268 TERADATA: PRODUCTS/SOLUTIONS OFFERED 297
TABLE 269 TERADATA: DEALS 298
11.2.13 H2O.AI 299
TABLE 270 H2O.AI: BUSINESS OVERVIEW 299
TABLE 271 H2O.AI: PRODUCTS/SOLUTIONS OFFERED 299
TABLE 272 H2O.AI: PRODUCT LAUNCHES 300
TABLE 273 H2O.AI: DEALS 301
11.2.14 DATAROBOT 302
TABLE 274 DATAROBOT: BUSINESS OVERVIEW 302
TABLE 275 DATAROBOT: PRODUCTS/SERVICES OFFERED 302
TABLE 276 DATAROBOT: DEALS 303
11.2.15 BIGML 304
TABLE 277 BIGML: BUSINESS OVERVIEW 304
TABLE 278 BIGML: PRODUCTS/SOLUTIONS OFFERED 304
TABLE 279 BIGML: PRODUCT LAUNCHES 305
TABLE 280 BIGML: DEALS 305
11.2.16 DATABRICKS 306
TABLE 281 DATABRICKS: BUSINESS OVERVIEW 306
TABLE 282 DATABRICKS: PRODUCTS/SOLUTIONS OFFERED 306
TABLE 283 DATABRICKS: PRODUCT LAUNCHES 307
TABLE 284 DATABRICKS: DEALS 307
11.2.17 DATAIKU 308
TABLE 285 DATAIKU: BUSINESS OVERVIEW 308
TABLE 286 DATAIKU: PRODUCTS/SOLUTIONS OFFERED 308
TABLE 287 DATAIKU: PRODUCT LAUNCHES 309
TABLE 288 DATAIKU: DEALS 310
11.2.18 MATHWORKS 311
TABLE 289 MATHWORKS: BUSINESS OVERVIEW 311
TABLE 290 MATHWORKS: PRODUCTS/SOLUTIONS OFFERED 311
TABLE 291 MATHWORKS: PRODUCT LAUNCHES 312
TABLE 292 MATHWORKS: DEALS 313
11.2.19 SPARKCOGNITION 314
TABLE 293 SPARKCOGNITION: BUSINESS OVERVIEW 314
TABLE 294 SPARKCOGNITION: PRODUCTS/SOLUTIONS OFFERED 314
TABLE 295 SPARKCOGNITION: PRODUCT LAUNCHES 315
TABLE 296 SPARKCOGNITION: DEALS 315
11.2.20 QLIK 317
TABLE 297 QLIK: BUSINESS OVERVIEW 317
TABLE 298 QLIK: PRODUCTS/SOLUTIONS OFFERED 317
TABLE 299 QLIK: PRODUCT LAUNCHES 318
TABLE 300 QLIK: DEALS 318
*Details on Business Overview, Products/Solutions offered, Recent Developments, MnM View might not be captured in case of unlisted companies.
11.3 OTHER PLAYERS 319
11.3.1 ALIBABA CLOUD 319
11.3.2 APPIER 320
11.3.3 SQUARK 321
11.3.4 AIBLE 321
11.3.5 DATAFOLD 322
11.3.6 BOOST.AI 322
11.3.7 TAZI AI 323
11.3.8 AKKIO 323
11.3.9 VALOHAI 324
11.3.10 DOTDATA 324
12 ADJACENT AND RELATED MARKETS 325
12.1 GENERATIVE AI MARKET 325
12.1.1 MARKET DEFINITION 325
12.1.2 MARKET OVERVIEW 325
TABLE 301 GLOBAL GENERATIVE AI MARKET SIZE AND GROWTH RATE, 2019–2022 (USD MILLION, Y-O-Y %) 325
TABLE 302 GLOBAL GENERATIVE AI MARKET SIZE AND GROWTH RATE, 2023–2028 (USD MILLION, Y-O-Y %) 326
12.1.3 GENERATIVE AI MARKET, BY OFFERING 326
TABLE 303 GENERATIVE AI MARKET, BY OFFERING, 2019–2022 (USD MILLION) 326
TABLE 304 GENERATIVE AI MARKET, BY OFFERING, 2023–2028 (USD MILLION) 326
12.1.4 GENERATIVE AI MARKET, BY APPLICATION 327
TABLE 305 GENERATIVE AI MARKET, BY APPLICATION, 2019–2022 (USD MILLION) 327
TABLE 306 GENERATIVE AI MARKET, BY APPLICATION, 2023–2028 (USD MILLION) 328
12.1.5 GENERATIVE AI MARKET, BY VERTICAL 329
TABLE 307 GENERATIVE AI MARKET, BY VERTICAL, 2019–2022 (USD MILLION) 329
TABLE 308 GENERATIVE AI MARKET, BY VERTICAL, 2023–2028 (USD MILLION) 330
12.1.6 GENERATIVE AI MARKET, BY REGION 330
TABLE 309 GENERATIVE AI MARKET, BY REGION, 2019–2022 (USD MILLION) 331
TABLE 310 GENERATIVE AI MARKET, BY REGION, 2023–2028 (USD MILLION) 331
12.2 ARTIFICIAL INTELLIGENCE MARKET 331
12.2.1 MARKET DEFINITION 331
12.2.2 MARKET OVERVIEW 332
12.2.3 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING 332
TABLE 311 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2016–2021 (USD BILLION) 332
TABLE 312 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2022–2027 (USD BILLION) 333
12.2.4 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY 333
TABLE 313 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2016–2021 (USD BILLION) 333
TABLE 314 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2022–2027 (USD BILLION) 333
12.2.5 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE 334
TABLE 315 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2016–2021 (USD BILLION) 334
TABLE 316 ARTIFICIAL INTELLIGENCE MARKET, BY DEPLOYMENT MODE, 2022–2027 (USD BILLION) 334
12.2.6 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE 334
TABLE 317 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE, 2016–2021 (USD BILLION) 335
TABLE 318 ARTIFICIAL INTELLIGENCE MARKET, BY ORGANIZATION SIZE, 2022–2027 (USD BILLION) 335
12.2.7 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION 335
TABLE 319 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2016–2021 (USD BILLION) 335
TABLE 320 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2022–2027 (USD BILLION) 336
12.2.8 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL 336
TABLE 321 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2016–2021 (USD BILLION) 336
TABLE 322 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2022–2027 (USD BILLION) 337
12.2.9 ARTIFICIAL INTELLIGENCE MARKET, BY REGION 337
TABLE 323 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2016–2021 (USD BILLION) 337
TABLE 324 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2022–2027 (USD BILLION) 338
13 APPENDIX 339
13.1 DISCUSSION GUIDE 339
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 344
13.3 CUSTOMIZATION OPTIONS 346
13.4 RELATED REPORTS 346
13.5 AUTHOR DETAILS 347