Summary

The global ModelOps Market is valued at USD 5.4 billion in 2024 and is estimated to reach USD 29.5 billion in 2029, registering a CAGR of 40.2% during the forecast period. The ModelOps Market focuses on optimizing the deployment, monitoring, and management of machine learning models in production. It encompasses automating model deployment, continuous monitoring for performance and data drift, ensuring governance and compliance, orchestrating automation for testing and retraining, and fostering collaboration among data scientists and stakeholders. This market is driven by the demand for scalable, reliable, and agile solutions across industries, enhancing operational efficiency and maximizing the value derived from AI initiatives. As AI and ML technologies advance, ModelOps continues to evolve with innovations in containerization, Kubernetes orchestration, and AI-driven automation, reshaping how organizations operationalize and derive insights from their models.
“By offering, the platforms segment is projected to hold the largest market size during the forecast period.”
In the rapidly evolving ModelOps market, platforms offering comprehensive solutions have seized the largest market share due to their integrated approach to managing the entire lifecycle of machine learning models. These platforms streamline operations by consolidating development, training, deployment, and monitoring processes into a unified environment, appealing to enterprises seeking efficiency and collaboration enhancements. Their scalability, supported by robust infrastructure and cloud capabilities, meets the increasing demand for deploying models at scale. Automation features throughout the lifecycle accelerate time-to-market and ensure consistency, while built-in governance mechanisms ensure compliance and reliability, crucial for regulated industries.
“By type, graph-based models are registered to grow at the highest CAGR during the forecast period.”
The rapid growth of graph-based model management tools within the ModelOps market stems from their adeptness at handling the intricate nature of modern AI systems. These tools manage complex relationships between models, datasets, and configurations, which traditional databases struggle to accommodate. Their scalability and flexibility make them ideal for dynamic AI environments where rapid evolution and large-scale data handling are the norm. Integrating seamlessly with existing AI platforms enhances visibility and control over model lifecycles, ensuring compliance with regulatory standards and internal governance. They support robust decision-making and automation in deployment processes by providing a clear and auditable lineage of models and data usage. As AI applications expand into new fields like edge computing and personalized medicine, graph-based tools offer a unified solution to effectively manage diverse and distributed environments.
“By application, the continuous integration/continuous deployment segment is projected to hold the largest market size during the forecast period.”
Continuous Integration and Continuous Delivery (CI/CD) holds a dominant position within the ModelOps market due to several key factors that highlight its critical role in deploying and managing machine learning models. First and foremost, CI/CD pipelines are foundational in enabling automation throughout the model development lifecycle. In the context of ModelOps, which focuses on operationalizing machine learning models at scale, CI/CD pipelines facilitate the seamless integration of new model versions into production environments. This automation streamlines the process of testing, building, packaging, and deploying models, reducing the manual effort and potential for human error, thereby increasing efficiency and reliability. Further, the demand for CI/CD in ModelOps is driven by the need for agility and speed in deploying models into production. Machine learning models often undergo iterative improvements based on real-world data feedback and evolving business requirements. CI/CD pipelines allow teams to continuously integrate these updates into the operational environment, ensuring that the latest versions of models are always available without disrupting existing processes
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 ModelOps 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: North America – 30%, Europe – 30%, Asia Pacific – 25%, Middle East & Africa - 10%, and Latin America - 5%
Major vendors offering modelOps solution and services across the globe are IBM (US), Google (US), Oracle (US), SAS Institute (US), AWS (US), Teradata (US), Palantir (US), Veritone (US), Altair (US), c3.ai (US), TIBCO (US), Databricks (US), Giggso (US), Verta (US), ModelOp (US), Comet ML (US), Superwise (Israel), Evidently Al (US), Minitab (US), Seldon (UK), Innominds (US), Datatron (US), Domino Data Lab (US), Arthur (US), Weights & Biases (US), Xenonstack (US), Cnvrg.io (Israel), DataKitchen (US), Haisten AI (US), Sparkling Logic (US), LeewayHertz (US).
Research Coverage
The market study covers modelOps across segments. It aims to estimate the market size and the growth potential across different segments, such as offering, model type, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, 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 with information on the closest approximations of the revenue numbers for the overall market for modelOps and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:

• Analysis of key drivers (Exponential rise of unstructured data, Rise in digitalization trend), restraints (Discrepancy among data sources impedes the advancement of modelOps, Data Security and Privacy Concerns), opportunities (Empowering modelOps through SDN-enabled network integration, Growing integration of advanced analytical functionalities), and challenges (Rise in need for training and upskilling to address the knowledge gap, Issues related to complexity and diversity of data collected)
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new solutions & service launches in the ModelOps Market.

• Market Development: Comprehensive information about lucrative markets – the report analyses the ModelOps Market across varied regions.

• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in ModelOps Market strategies; the report also helps stakeholders understand the pulse of the ModelOps 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), Oracle (US), SAS Institute(US), Google (US), and AWS (US) among others, in the ModelOps Market.

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Table of Contents

1 INTRODUCTION 28
1.1 STUDY OBJECTIVES 28
1.2 MARKET DEFINITION 28
1.2.1 INCLUSIONS AND EXCLUSIONS 28
1.3 MARKET SCOPE 30
1.3.1 MARKET SEGMENTATION 30
1.3.2 REGIONS COVERED 31
1.3.3 YEARS CONSIDERED 31
1.4 CURRENCY CONSIDERED 32
1.5 STAKEHOLDERS 32
1.6 RECESSION IMPACT 32
2 RESEARCH METHODOLOGY 33
2.1 RESEARCH DATA 33
2.1.1 SECONDARY DATA 34
2.1.2 PRIMARY DATA 34
2.1.2.1 Breakup of primary interviews 35
2.1.2.2 Key industry insights 35
2.2 DATA TRIANGULATION 36
2.3 MARKET SIZE ESTIMATION 37
2.3.1 TOP-DOWN APPROACH 37
2.3.2 BOTTOM-UP APPROACH 38
2.4 MARKET FORECAST 42
2.5 RESEARCH ASSUMPTIONS 42
2.6 RESEARCH LIMITATIONS 44
2.7 IMPLICATION OF RECESSION ON GLOBAL MODELOPS MARKET 44
3 EXECUTIVE SUMMARY 45
4 PREMIUM INSIGHTS 51
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN MODELOPS MARKET 51
4.2 OVERVIEW OF RECESSION IN MODELOPS MARKET 51
4.3 MODELOPS MARKET, BY KEY APPLICATIONS, 2024–2029 52
4.4 MODELOPS MARKET, BY KEY MODEL TYPES AND APPLICATIONS, 2024 52
4.5 MODELOPS MARKET, BY REGION, 2024 53
 
5 MARKET OVERVIEW AND INDUSTRY TRENDS 54
5.1 INTRODUCTION 54
5.2 MARKET DYNAMICS 54
5.2.1 DRIVERS 55
5.2.1.1 Integration of ModelOps with DevOps and DataOps 55
5.2.1.2 Rising demand for Explainable AI (XAI) 56
5.2.1.3 Increasing need to address model drift with ModelOps solutions 56
5.2.1.4 Rising demand for automated monitoring and alerting capabilities 56
5.2.2 RESTRAINTS 57
5.2.2.1 Shortage of skilled professionals 57
5.2.2.2 Model interpretability and explainability 57
5.2.3 OPPORTUNITIES 58
5.2.3.1 Integration of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines 58
5.2.3.2 Enhancements in model versioning and lifecycle management 58
5.2.4 CHALLENGES 59
5.2.4.1 Difficulty in managing intricate dependencies 59
5.2.4.2 Complexities of integrating with existing systems 59
5.2.4.3 Disconnect between insights and action 59
5.3 CASE STUDY ANALYSIS 60
5.3.1 CASE STUDY 1: SCRIBD ACCELERATES MODEL DELIVERY USING VERTA’S MACHINE LEARNING OPERATIONS PLATFORM 60
5.3.2 CASE STUDY 2: EXSCIENTIA SHORTENS MODEL MONITORING AND PREPARATION FROM DAYS TO HOURS 60
5.3.3 CASE STUDY 3: RBC CAPITAL MARKETS ENHANCES BOND TRADING EFFICIENCY USING AI AND MODELOPS CENTER 61
5.3.4 CASE STUDY 4: M-KOPA REVOLUTIONIZES MODEL MANAGEMENT PROCESS WITH ASSISTANCE OF W&B 61
5.3.5 CASE STUDY 5: CLEARSCAPE ANALYTICS EXPEDITES DEVELOPMENT OF CREDIT RISK PORTFOLIO MODELS FOR SICREDI 62
5.3.6 CASE STUDY 6: ENHANCING ML EXPERIMENT MANAGEMENT AT UBER WITH COMET 62
5.3.7 CASE STUDY 7: ACCELERATED AI INTEGRATION FOR ENHANCED EVENT RECOMMENDATIONS BY CNVRG.IO 63
5.4 EVOLUTION OF MODELOPS MARKET 64
5.5 ECOSYSTEM ANALYSIS 65
5.5.1 PLATFORM PROVIDERS 67
5.5.2 SERVICE PROVIDERS 67
5.5.3 END USERS 68
5.5.4 REGULATORY BODIES 68
 
5.6 TECHNOLOGY ANALYSIS 68
5.6.1 KEY TECHNOLOGIES 68
5.6.1.1 Artificial intelligence 68
5.6.1.2 Cloud computing 69
5.6.1.3 Knowledge graphs 69
5.6.1.4 No code 69
5.6.2 ADJACENT TECHNOLOGIES 69
5.6.2.1 Big data & analytics 69
5.6.2.2 Edge computing 70
5.7 SUPPLY CHAIN ANALYSIS 70
5.8 REGULATORY LANDSCAPE 71
5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 72
5.8.2 REGULATIONS: MODELOPS 74
5.8.2.1 North America 74
5.8.2.1.1 US 74
5.8.2.1.2 Canada 75
5.8.2.2 Europe 75
5.8.2.3 Asia Pacific 75
5.8.2.3.1 Singapore 75
5.8.2.3.2 China 75
5.8.2.3.3 India 75
5.8.2.3.4 Japan 76
5.8.2.4 Middle East & Africa 76
5.8.2.4.1 UAE 76
5.8.2.4.2 KSA 76
5.8.2.4.3 South Africa 76
5.8.2.5 Latin America 76
5.8.2.5.1 Brazil 76
5.8.2.5.2 Mexico 77
5.9 PATENT ANALYSIS 77
5.9.1 METHODOLOGY 77
5.9.2 PATENTS FILED, BY DOCUMENT TYPE 77
5.9.3 INNOVATIONS AND PATENT APPLICATIONS 78
5.9.3.1 Patent applicants 78
5.10 KEY CONFERENCES AND EVENTS, 2024-2025 82
5.11 PORTER’S FIVE FORCES ANALYSIS 83
5.11.1 THREAT FROM NEW ENTRANTS 84
5.11.2 THREAT OF SUBSTITUTES 84
5.11.3 BARGAINING POWER OF SUPPLIERS 84
5.11.4 BARGAINING POWER OF BUYERS 84
5.11.5 INTENSITY OF COMPETITIVE RIVALRY 84
5.12 PRICING ANALYSIS 84
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION 84
5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING 86
5.13 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 87
5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 88
5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS 88
5.14.2 BUYING CRITERIA 88
5.15 INVESTMENT AND FUNDING SCENARIO 89
5.16 MODELOPS VS. MLOPS 90
5.17 MODELOPS BEST PRACTICES 91
6 MODELOPS MARKET, BY OFFERING 92
6.1 INTRODUCTION 93
6.1.1 OFFERING: MODELOPS MARKET DRIVERS 93
6.2 PLATFORMS 95
6.2.1 OPTIMIZING MACHINE LEARNING MODEL LIFECYCLE MANAGEMENT WITH MODELOPS PLATFORMS 95
6.2.2 TYPE 96
6.2.2.1 Development & experimentation platforms 97
6.2.2.2 Monitoring & observability tools 98
6.2.2.3 Automated machine learning (AutoML) platforms 99
6.2.2.4 Performance tracking & management platforms 100
6.2.2.5 Model explainability & interpretability tools 101
6.2.2.6 Serving & deployment tools 102
6.2.2.7 Others 103
6.2.3 DEPLOYMENT MODE 104
6.2.3.1 Cloud 105
6.2.3.2 On-premises 106
6.3 SERVICES 107
6.3.1 ELEVATING DATA INSIGHTS WITH MODELOPS SERVICES 107
6.3.2 CONSULTING 109
6.3.3 DEPLOYMENT & INTEGRATION 110
6.3.4 SUPPORT & MAINTENANCE 111
7 MODELOPS MARKET, BY MODEL TYPE 112
7.1 INTRODUCTION 113
7.1.1 MODEL TYPE: MODELOPS MARKET DRIVERS 113
7.2 ML MODELS 114
7.2.1 SEGMENTING, FORECASTING, AND OPTIMIZING MODELOPS FOR COMPETITIVE ADVANTAGE 114
7.3 GRAPH-BASED MODELS 115
7.3.1 GRAPH-BASED MODELS ENHANCE PREDICTIONS AND DECISION-MAKING IN MODELOPS 115
7.4 RULE & HEURISTIC MODELS 116
7.4.1 OPTIMIZING MODELOPS WITH RULE-BASED, HEURISTIC, AND HYBRID MODELS 116
7.5 LINGUISTIC MODELS 117
7.5.1 OPTIMIZING LINGUISTIC MODELS FOR EFFICIENT NLP DEPLOYMENT AND GOVERNANCE 117
7.6 AGENT-BASED MODELS 118
7.6.1 ENHANCING STRATEGIC DECISION-MAKING THROUGH ADVANCED AGENT-BASED MODEL SIMULATION 118
7.7 BRING YOUR OWN MODELS 119
7.7.1 MAXIMIZING OPERATIONAL EFFICIENCY THROUGH SEAMLESS INTEGRATION OF DIVERSE AI MODELS 119
7.8 OTHER MODEL TYPES 120
8 MODELOPS MARKET, BY APPLICATION 122
8.1 INTRODUCTION 123
8.1.1 APPLICATION: MODELOPS MARKET DRIVERS 123
8.2 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT 125
8.2.1 IMPLEMENTATION OF CI/CD FOR ACCELERATED DEPLOYMENT OF MACHINE LEARNING MODELS IN MODELOPS 125
8.3 MONITORING & ALERTING 126
8.3.1 ENHANCING MODELOPS WITH RELIABLE MONITORING & ALERTING SERVICES 126
8.4 DASHBOARD & REPORTING 127
8.4.1 DASHBOARD AND REPORTING ENHANCE OPERATIONAL PROCESSES SURROUNDING MACHINE LEARNING MODELS 127
8.5 MODEL LIFECYCLE MANAGEMENT 128
8.5.1 MAXIMIZING AI VALUE THROUGH EFFECTIVE MODEL LIFECYCLE MANAGEMENT 128
8.6 GOVERNANCE, RISK, & COMPLIANCE 129
8.6.1 IMPLEMENTATION OF ROBUST GOVERNANCE, RISK, AND COMPLIANCE (GRC) FRAMEWORK IN MODELOPS FOR EFFECTIVE AI MODEL MANAGEMENT 129
8.7 PARALLELIZATION & DISTRIBUTED COMPUTING 130
8.7.1 EMPOWERING AI/ML SCALABILITY WITH PARALLELIZATION AND DISTRIBUTED COMPUTING IN MODELOPS 130
8.8 BATCH SCORING 131
8.8.1 ENHANCING DATA-DRIVEN DECISION-MAKING WITH BATCH SCORING IN MODELOPS 131
8.9 OTHER APPLICATIONS 132
9 MODELOPS MARKET, BY VERTICAL 133
9.1 INTRODUCTION 134
9.1.1 VERTICAL: MODELOPS MARKET DRIVERS 134
9.2 BFSI 136
9.2.1 OPTIMIZING MODELOPS FOR BFSI SECTOR ADVANCEMENTS 136
9.3 TELECOMMUNICATIONS 138
9.3.1 IMPLEMENTING MODELOPS FOR ENHANCED TELECOMMUNICATION EFFICIENCY 138
9.4 RETAIL & ECOMMERCE 139
9.4.1 STREAMLINING AI AND ML DEPLOYMENT TO REVOLUTIONIZE RETAIL AND ECOMMERCE OPERATIONS FOR ENHANCED EFFICIENCY AND CUSTOMER EXPERIENCE 139
9.5 HEALTHCARE & LIFE SCIENCES 140
9.5.1 ENHANCING PATIENT OUTCOMES AND MEDICAL INNOVATION THROUGH MODELOPS IN HEALTHCARE AND LIFE SCIENCES 140
9.6 GOVERNMENT & DEFENSE 142
9.6.1 GOVERNMENTS USE MODELOPS TO APPLY REAL-TIME ANALYTICS IN MISSION-CRITICAL SCENARIOS 142
9.7 IT/ITES 143
9.7.1 IMPLEMENTING MODELOPS FOR EFFICIENT AI/ML LIFECYCLE MANAGEMENT IN IT/ITES 143
9.8 ENERGY & UTILITIES 144
9.8.1 IMPLEMENTING MODELOPS FOR ENERGY AND UTILITIES OPTIMIZATION 144
9.9 MANUFACTURING 146
9.9.1 DEPLOYING MODELOPS FOR ENHANCED MANUFACTURING EFFICIENCY 146
9.10 TRANSPORTATION & LOGISTICS 147
9.10.1 ENHANCING EFFICIENCY AND SAFETY THROUGH MODELOPS IN TRANSPORTATION AND LOGISTICS 147
9.11 OTHER VERTICALS 148
10 MODELOPS MARKET, BY REGION 150
10.1 INTRODUCTION 151
10.2 NORTH AMERICA 152
10.2.1 NORTH AMERICA: MODELOPS MARKET DRIVERS 153
10.2.2 NORTH AMERICA: RECESSION IMPACT 153
10.2.3 US 160
10.2.3.1 Widespread adoption of AI and ML technologies across industries to drive market 160
10.2.4 CANADA 163
10.2.4.1 Rising demand for AI and ML solutions in various sectors to drive market 163
10.3 EUROPE 165
10.3.1 EUROPE: MODELOPS MARKET DRIVERS 166
10.3.2 EUROPE: RECESSION IMPACT 166
10.3.3 UK 172
10.3.3.1 Increasing AI adoption across industries to drive market 172
10.3.4 GERMANY 175
10.3.4.1 Increasing adoption of AI and ML technologies to drive market 175
10.3.5 FRANCE 175
10.3.5.1 Rising focus on operationalizing AI and ML models to drive market 175
10.3.6 ITALY 176
10.3.6.1 Growing integration of AI and ML across diverse sectors to drive market 176
10.3.7 SPAIN 176
10.3.7.1 Increasing reliance on data-driven decision-making across industries to drive market 176
10.3.8 REST OF EUROPE 176
10.4 ASIA PACIFIC 177
10.4.1 ASIA PACIFIC: MODELOPS MARKET DRIVERS 177
10.4.2 ASIA PACIFIC: RECESSION IMPACT 177
10.4.3 CHINA 184
10.4.3.1 Rising focus on operationalizing AI models and enhancing business outcomes to drive market 184
10.4.4 JAPAN 187
10.4.4.1 Increasing adoption of AI and ML models in various industries to drive market 187
10.4.5 INDIA 187
10.4.5.1 Rising adoption of AI technologies across sectors to drive market 187
10.4.6 SOUTH KOREA 188
10.4.6.1 Increasing adoption of AI across sectors to drive market 188
10.4.7 AUSTRALIA & NEW ZEALAND 188
10.4.7.1 Growing emphasis on integrating AI solutions to enhance operational efficiency to drive market 188
10.4.8 REST OF ASIA PACIFIC 188
10.5 MIDDLE EAST & AFRICA 189
10.5.1 MIDDLE EAST & AFRICA: MODELOPS MARKET DRIVERS 189
10.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT 189
10.5.3 UAE 195
10.5.3.1 Government initiatives toward building knowledge-based economy to drive market 195
10.5.4 KSA 196
10.5.4.1 Growing emphasis on digital transformation and AI integration across sectors to drive market 196
10.5.5 QATAR 196
10.5.5.1 Rising adoption of AI and ML technologies across sectors to drive market 196
10.5.6 EGYPT 197
10.5.6.1 Increasing investments by companies to operationalize AI and ML models to drive market 197
10.5.7 SOUTH AFRICA 197
10.5.7.1 Growing adoption of AI and machine learning models in various sectors to drive market 197
10.5.8 REST OF MIDDLE EAST & AFRICA 198
 
10.6 LATIN AMERICA 198
10.6.1 LATIN AMERICA: MODELOPS MARKET DRIVERS 198
10.6.2 LATIN AMERICA: RECESSION IMPACT 199
10.6.3 BRAZIL 205
10.6.3.1 Technological advancements and regulatory compliance to drive market 205
10.6.4 MEXICO 205
10.6.4.1 Increasing digital transformation efforts across industries to drive market 205
10.6.5 ARGENTINA 206
10.6.5.1 Increasing adoption of machine learning and AI technologies in various sectors to drive market 206
10.6.6 REST OF LATIN AMERICA 206
11 COMPETITIVE LANDSCAPE 207
11.1 OVERVIEW 207
11.2 STRATEGIES ADOPTED BY KEY PLAYERS 207
11.3 REVENUE ANALYSIS 209
11.4 MARKET SHARE ANALYSIS 210
11.4.1 MARKET RANKING ANALYSIS 211
11.5 PRODUCT COMPARATIVE ANALYSIS 214
11.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 215
11.6.1 STARS 215
11.6.2 EMERGING LEADERS 215
11.6.3 PERVASIVE PLAYERS 215
11.6.4 PARTICIPANTS 215
11.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 217
11.6.5.1 Company footprint 217
11.6.5.2 Offering footprint 218
11.6.5.3 Application footprint 218
11.6.5.4 Regional footprint 219
11.6.5.5 Vertical footprint 219
11.7 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 220
11.7.1 PROGRESSIVE COMPANIES 220
11.7.2 RESPONSIVE COMPANIES 220
11.7.3 DYNAMIC COMPANIES 220
11.7.4 STARTING BLOCKS 221
11.7.5 COMPETITIVE BENCHMARKING: START-UPS/SMES, 2023 222
11.8 COMPETITIVE SCENARIOS AND TRENDS 224
11.8.1 PRODUCT LAUNCHES & ENHANCEMENTS 224
11.8.2 DEALS 225
11.9 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS 226
12 COMPANY PROFILES 228
12.1 INTRODUCTION 228
12.2 KEY PLAYERS 228\
(Business Overview, Products/Solutions/Services offered, Recent Developments, MnM View)*
12.2.1 IBM 228
12.2.2 GOOGLE 232
12.2.3 SAS INSTITUTE 236
12.2.4 AWS 239
12.2.5 ORACLE 242
12.2.6 TERADATA 246
12.2.7 VERITONE 249
12.2.8 ALTAIR 252
12.2.9 C3.AI 255
12.2.10 PALANTIR 258
12.2.11 TIBCO SOFTWARE 261
12.2.12 DOMINO DATA LAB 263
12.2.13 DATABRICKS 266
12.2.14 GIGGSO 267
12.2.15 MODELOP 268
12.3 OTHER PLAYERS 269
12.3.1 VERTA 269
12.3.2 COMET ML 270
12.3.3 SUPERWISE 271
12.3.4 EVIDENTLY AI 272
12.3.5 MINITAB 273
12.3.6 SELDON 274
12.3.7 INNOMINDS 275
12.3.8 DATATRON 276
12.3.9 ARTHUR AI 277
12.3.10 WEIGHTS & BIASES 278
12.3.11 XENONSTACK 279
12.3.12 CNVRG.IO 280
12.3.13 DATAKITCHEN 281
12.3.14 HAISTEN AI 282
12.3.15 SPARKLING LOGIC 283
12.3.16 LEEWAYHERTZ 284
*Details on Business Overview, Products/Solutions/Services offered, Recent Developments, MnM View might not be captured in case of unlisted companies.
 
13 ADJACENT AND RELATED MARKETS 285
13.1 INTRODUCTION 285
13.2 MLOPS 285
13.2.1 MARKET DEFINITION 285
13.2.2 MARKET OVERVIEW 285
13.2.2.1 MLOps market, by component 285
13.2.2.2 MLOps market, by deployment mode 286
13.2.2.3 MLOps market, by organization size 287
13.2.2.4 MLOps market, by vertical 287
13.2.2.5 MLOps market, by region 289
13.3 ARTIFICIAL INTELLIGENCE (AI) MARKET 290
13.3.1 MARKET DEFINITION 290
13.3.2 MARKET OVERVIEW 290
13.3.2.1 Artificial intelligence (AI) market, by offering 291
13.3.2.2 Artificial intelligence (AI) market, by hardware 292
13.3.2.3 Artificial intelligence (AI) market, by software 293
13.3.2.4 Artificial intelligence (AI) market, by services 294
13.3.2.5 Artificial intelligence (AI) market, by technology 295
13.3.2.6 Artificial intelligence (AI) market, by business function 296
13.3.2.7 Artificial intelligence (AI) market, by vertical 297
13.3.2.8 Artificial intelligence (AI) market, by region 299
14 APPENDIX 300
14.1 DISCUSSION GUIDE 300
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 306
14.3 CUSTOMIZATION OPTIONS 308
14.4 RELATED REPORTS 308
14.5 AUTHOR DETAILS 309