ModelOps Market

ModelOps Market Size, Share, Growth Analysis, By Offering (Platforms & Services), Application (CI/CD, Monitoring & Alerting), Model Type (ML Model, Graph Model, Agent-based Model), Vertical and Region - Global Industry Forecast to 2029

Report Code: TC 9051 Jun, 2024, by marketsandmarkets.com

[309 Pages Report] The global ModelOps Market is projected to grow from USD 5.4 billion in 2024 to USD 29.5 billion in 2029, at a CAGR of 40.2% during the forecast period. In today's digital world, the increase in data volume is a crucial requirement for advanced analytics solutions to process, analyze, and interpret vast datasets efficiently. ModelOps is crucial in modern data-driven businesses for several reasons. It ensures that machine learning models, which are often complex and sensitive to changes in data and environment, continue to perform accurately and reliably in production. By implementing rigorous deployment, monitoring, and maintenance practices through ModelOps, organizations can minimize the risk of model degradation and ensure consistent performance over time.

ModelOps Market Market

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ModelOps Market Market Opportunities

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Market Dynamics

Driver: Integration of ModelOps with DevOps and DataOps

Integrating ModelOps with established DevOps and DataOps practices represents a significant market driver by fostering a seamless pipeline for developing, deploying, and monitoring machine learning models. This harmonization streamlines workflows and bridges the traditionally separate domains of data science and IT operations. Organizations can reduce time-to-market, minimize errors, and enhance model reliability by automating the lifecycle from model development to production deployment. Continuous monitoring and maintenance, crucial for adapting to evolving data patterns, benefits from DevOps' robust monitoring capabilities and DataOps' focus on data quality and governance. This integration allows data scientists to concentrate on refining models while IT teams ensure effective deployment and maintenance, enhancing productivity and aligning models with business objectives. Additionally, it supports scalability, enabling efficient management of increasing models and data sources, crucial for driving innovation and competitive advantage through advanced analytics. Thus, integrating ModelOps with DevOps and DataOps practices is a key market driver that enhances operational efficiency, ensures continuous model performance, and fosters collaboration between data science and IT operations.

Restraint: Model interpretability and explainability

Integrating machine learning models into decision-making processes necessitates a robust understanding of their operations, fueling the demand for interpretability and explainability. These concepts ensure transparency, trust, and accountability, especially in high-stakes domains like healthcare, finance, and criminal justice. However, achieving interpretability without compromising performance is challenging, particularly for complex models such as deep neural networks. These models, known for their high accuracy and ability to handle large, unstructured data, operate as "black boxes," with intricate internal structures and numerous difficult parameters. Simplifying these models to enhance interpretability often leads to a trade-off, reducing their predictive power and effectiveness. Techniques like feature importance analysis, LIME (Local Interpretable Model-agnostic Explanations), and SHAP (Shapley Additive exPlanations) provide some insights but may still fall short of fully elucidating the decision-making process of highly complex models. Moreover, the reliance on post-hoc interpretability methods can introduce another layer of approximation, potentially leading to misinterpretations. Balancing the dual objectives of interpretability and performance requires innovative approaches, such as developing inherently interpretable models or integrating interpretability directly into the design phase of complex models. However, these solutions are still evolving and often necessitate a compromise, highlighting an ongoing restraint in the practical deployment of sophisticated machine learning systems in real-world decision-making scenarios. This underscores the need for continued research and development in methods that enhance model transparency without significantly diminishing performance capabilities.

Opportunity: Integration of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines

The integration of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines for machine learning models is revolutionizing the ModelOps market, which focuses on the operationalization and management of these models. By streamlining the processes of testing, validating, and deploying models, automated CI/CD pipelines reduce human intervention and errors, accelerating the time-to-market and enhancing the reliability and performance of AI solutions. Continuous integration ensures robust models through automatic testing of every change, while continuous deployment facilitates rapid updates, enabling quick adaptation to business needs. Containerization technologies like Docker and Kubernetes further enhance scalability and reproducibility; Docker packages models with dependencies for consistent performance across environments, and Kubernetes efficiently manages resources and scaling. These capabilities enable organizations to operationalize AI at scale, maintain high model performance, and swiftly respond to new data, ultimately achieving greater and sustained returns on AI investments in a competitive, data-driven world.

Challenge: Challenge due to the intricate dependencies

In ModelOps, ensuring consistent and reproducible environments is particularly challenging due to the intricate dependencies on specific versions of libraries, frameworks, and data sources. Machine learning models rely on numerous frequently updated libraries and frameworks, where version discrepancies can lead to inconsistent model behavior across different environments. This necessitates meticulous dependency management to prevent compatibility issues and unexpected errors during deployment. Additionally, models are trained on specific datasets, which can change over time, complicating the task of maintaining model accuracy with new data. Robust versioning and tracking of data sources are essential to address this. The heterogeneity of development, testing, and production environments—each potentially having different hardware, software configurations, and infrastructure—adds another layer of complexity. Ensuring consistency across these environments is crucial for reliable model performance. Thus, the primary restraint in ModelOps is managing these dependencies to achieve consistent, reproducible, and reliable environments throughout the model lifecycle. This requires robust versioning strategies, automated testing pipelines, and diligent monitoring to ensure seamless operation from development to production.

ModelOps Market Ecosystem

The ModelOps Market ecosystem comprises Platform providers, Model lifecycle management providers, service providers, end users and regulatory bodies. These vendors are equally competent and offer innovative technology bundled with modelOps. This segmented ecosystem works collaboratively to drive the transition toward more efficient workflows and output generation, leveraging technology and data to achieve goals.

Top Companies in ModelOps Market Market

By vertical, BFSI segment accounts for the largest market size during the forecast period.

The BFSI sector leads the ModelOps market mainly due to its deep integration of complex models for critical functions such as risk management, fraud detection, and personalized financial services. These models are pivotal for decision-making and regulatory compliance, making efficient ModelOps practices crucial. BFSI's stringent regulatory environment, including frameworks like Basel III and GDPR, necessitates rigorous model validation and monitoring throughout their lifecycle. This regulatory pressure compels BFSI firms to invest significantly in robust ModelOps solutions to ensure compliance and operational efficiency. Additionally, the scale of operations in BFSI demands sophisticated ModelOps capabilities to manage numerous models across diverse departments and geographies effectively. Centralized management through ModelOps streamlines deployment, version control, and performance monitoring, optimizing operational efficiency and scalability. Moreover, intense competition within BFSI drives firms to leverage advanced analytics and AI-driven insights from models, further propelling the demand for agile and effective ModelOps frameworks.

By application, the monitoring and altering segment is projected to grow at the highest CAGR during the forecast period.

The Application Monitoring and Alerting segment leads in the ModelOps market with the highest CAGR due to its critical role in supporting the widespread adoption of AI and machine learning models across various industries. As these models are deployed in real-world applications, continuous monitoring and timely alerts for anomalies are essential to maintain their accuracy and reliability. The complexity of managing the entire model lifecycle—from development and testing to deployment and ongoing operations—further amplifies the need for robust monitoring solutions. These tools optimize operational efficiency by detecting issues early and ensuring compliance with regulatory standards by tracking model behavior and identifying biases. Moreover, they integrate seamlessly with DevOps practices, promoting collaboration between data scientists, engineers, and operations teams. The rapid advancements in explainable AI and model interpretability technologies further emphasize the importance of sophisticated monitoring and alerting capabilities in providing transparent insights into model decisions.

North America to account for the largest market size during the forecast period.

North America dominates the ModelOps market primarily due to its advanced technology infrastructure, including robust cloud computing and analytics platforms, which facilitate efficient deployment and management of machine learning models. The region hosts a concentration of leading global enterprises across diverse industries, driving demand for ModelOps solutions to streamline model deployment and ensure regulatory compliance. Moreover, North America benefits from a supportive regulatory environment and a skilled workforce in data science and machine learning engineering, fostering innovation and adopting ModelOps practices. Academic and research institutions further contribute to the region's leadership by advancing ModelOps methodologies. These factors collectively position North America as a pivotal hub for ModelOps, enabling organizations to leverage cutting-edge technologies and strategies for effective model operationalization and management at scale across various sectors.

North American ModelOps Market Market Size, and Share

Top Companies in ModelOps Market

The major modelOps Platform and service providers include 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 AI (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). These companies have used organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the ModelOps Market.

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Scope of the Report

Report Metrics

Details

Market size available for years

2019–2029

Base year considered

2023

Forecast period

2024–2029

Forecast units

USD Billion

Segments Covered

Offering, Model Type, Application, Vertical, and Region

Geographies covered

North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

List of Companies covered

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).

This research report categorizes the ModelOps Market based on Offering, Model Type, Application, Vertical, and Region.

Offering:
  • Platforms
    • By type
    • Development & Experimentation Platforms
    • Monitoring & Observability Tools
    • Automated Machine Learning (AutoML) Platforms
    • Performance Tracking & Management Platforms
    • Model Explainability & Interpretability Tools
    • Serving & Deployment Tools
    • Other types
  • By Deployment Mode
    • Cloud
    • On-Premises
  • Services
    • Consulting Services
    • Deployment & Integration
    • System & Maintenance
By Model Type:
  • ML Models
  • Graph-Based Models
  • Rule & Heuristic Models
  • Linguistic Models
  • Agent-Based Models
  • Bring Your Own Models
  • Other Model Types
By Application:
  • Continuous Integration/Continuous Deployment
  • Batch Scoring
  • Governance, Risk and Compliance
  • Parallelization & Distributed Computing
  • Monitoring & Alerting
  • Dashboard & Reporting
  • Model Lifecycle Management
  • Other Application
By Verticals:
  • BFSI
  • Retail & eCommerce
  • Healthcare & Life sciences
  • Telecommunications
  • IT/ITeS
  • Energy & Utilities  
  • Manufacturing
  • Transportation & Logistics
  • Government & Defense
  • Other Verticals
By Region:
  • North America
    • US
    • Canada
  • Europe
    • UK
    • France
    • Germany
    • Italy
    • Spain
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • ANZ
    • Rest of Asia Pacific
  • Middle East & Africa
    • Kingdom of Saudi Arabia
    • UAE
    • Qatar
    • Egypt
    • South Africa
    • Rest of the Middle East & Africa
  • Latin America
    • Brazil
    • Mexico
    • Argentina
    • Rest of Latin America

Recent Developments:

  • In April 2024, IBM Updated Watson Studio on Cloud Pak for Data 4.8 with features enhancing integration with IBM Knowledge Catalog, improving tools like Data Refinery and JupyterLab IDE, and adding new capabilities such as federated learning and pipeline automation.
  • In April 2024, IBM acquired HashiCorp for USD 6.4 billion to enhance its hybrid cloud and AI capabilities. This acquisition integrated HashiCorp’s infrastructure automation tools, such as Terraform, into IBM’s portfolio, driving synergies with IBM’s Red Hat and watsonx offerings and expanding its modelOps capabilities. The deal aims to streamline lifecycle management across multi-cloud environments.
  • In March 2024, SAS and Microsoft partnered with Microsoft Azure to integrate SAS Viya, an analytics platform, enabling customers to leverage advanced AI and ML capabilities on the cloud. This partnership aims to streamline AI and ML model operations, empowering organizations to accelerate their journey toward deploying and managing analytics solutions effectively.
  • In November 2023, C3 AI expanded its collaboration with Amazon in artificial intelligence (AI). This partnership signifies a deepening integration of C3 AI's AI technologies with Amazon's cloud infrastructure, likely leading to enhanced AI capabilities and market opportunities for both companies.
  • In July 2022, TIBCO introduced a new ModelOps platform to streamline the deployment and management of machine learning models. The platform likely integrates automation and collaboration features to accelerate model delivery and improve operational efficiency for data science teams.

Frequently Asked Questions (FAQ):

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TABLE OF CONTENTS
 
1 INTRODUCTION (Page No. - 28)
    1.1 STUDY OBJECTIVES 
    1.2 MARKET DEFINITION 
           1.2.1 INCLUSIONS AND EXCLUSIONS
    1.3 MARKET SCOPE 
           1.3.1 MARKET SEGMENTATION
           1.3.2 REGIONS COVERED
           1.3.3 YEARS CONSIDERED
    1.4 CURRENCY CONSIDERED 
    1.5 STAKEHOLDERS 
    1.6 RECESSION IMPACT 
 
2 RESEARCH METHODOLOGY (Page No. - 33)
    2.1 RESEARCH DATA 
           2.1.1 SECONDARY DATA
           2.1.2 PRIMARY DATA
                    2.1.2.1 Breakup of primary interviews
                    2.1.2.2 Key industry insights
    2.2 DATA TRIANGULATION 
    2.3 MARKET SIZE ESTIMATION 
           2.3.1 TOP-DOWN APPROACH
           2.3.2 BOTTOM-UP APPROACH
    2.4 MARKET FORECAST 
    2.5 RESEARCH ASSUMPTIONS 
    2.6 RESEARCH LIMITATIONS 
    2.7 IMPLICATION OF RECESSION ON GLOBAL MODELOPS MARKET 
 
3 EXECUTIVE SUMMARY (Page No. - 45)
 
4 PREMIUM INSIGHTS (Page No. - 51)
    4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN MODELOPS MARKET 
    4.2 OVERVIEW OF RECESSION IN MODELOPS MARKET 
    4.3 MODELOPS MARKET, BY KEY APPLICATIONS, 2024–2029 
    4.4 MODELOPS MARKET, BY KEY MODEL TYPES AND APPLICATIONS, 2024 
    4.5 MODELOPS MARKET, BY REGION, 2024 
 
5 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 54)
    5.1 INTRODUCTION 
    5.2 MARKET DYNAMICS 
           5.2.1 DRIVERS
                    5.2.1.1 Integration of ModelOps with DevOps and DataOps
                    5.2.1.2 Rising demand for Explainable AI (XAI)
                    5.2.1.3 Increasing need to address model drift with ModelOps solutions
                    5.2.1.4 Rising demand for automated monitoring and alerting capabilities
           5.2.2 RESTRAINTS
                    5.2.2.1 Shortage of skilled professionals
                    5.2.2.2 Model interpretability and explainability
           5.2.3 OPPORTUNITIES
                    5.2.3.1 Integration of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines
                    5.2.3.2 Enhancements in model versioning and lifecycle management
           5.2.4 CHALLENGES
                    5.2.4.1 Difficulty in managing intricate dependencies
                    5.2.4.2 Complexities of integrating with existing systems
                    5.2.4.3 Disconnect between insights and action
    5.3 CASE STUDY ANALYSIS 
           5.3.1 CASE STUDY 1: SCRIBD ACCELERATES MODEL DELIVERY USING VERTA’S MACHINE LEARNING OPERATIONS PLATFORM
           5.3.2 CASE STUDY 2: EXSCIENTIA SHORTENS MODEL MONITORING AND PREPARATION FROM DAYS TO HOURS
           5.3.3 CASE STUDY 3: RBC CAPITAL MARKETS ENHANCES BOND TRADING EFFICIENCY USING AI AND MODELOPS CENTER
           5.3.4 CASE STUDY 4: M-KOPA REVOLUTIONIZES MODEL MANAGEMENT PROCESS WITH ASSISTANCE OF W&B
           5.3.5 CASE STUDY 5: CLEARSCAPE ANALYTICS EXPEDITES DEVELOPMENT OF CREDIT RISK PORTFOLIO MODELS FOR SICREDI
           5.3.6 CASE STUDY 6: ENHANCING ML EXPERIMENT MANAGEMENT AT UBER WITH COMET
           5.3.7 CASE STUDY 7: ACCELERATED AI INTEGRATION FOR ENHANCED EVENT RECOMMENDATIONS BY CNVRG.IO
    5.4 EVOLUTION OF MODELOPS MARKET 
    5.5 ECOSYSTEM ANALYSIS 
           5.5.1 PLATFORM PROVIDERS
           5.5.2 SERVICE PROVIDERS
           5.5.3 END USERS
           5.5.4 REGULATORY BODIES
    5.6 TECHNOLOGY ANALYSIS 
           5.6.1 KEY TECHNOLOGIES
                    5.6.1.1 Artificial intelligence
                    5.6.1.2 Cloud computing
                    5.6.1.3 Knowledge graphs
                    5.6.1.4 No code
           5.6.2 ADJACENT TECHNOLOGIES
                    5.6.2.1 Big data & analytics
                    5.6.2.2 Edge computing
    5.7 SUPPLY CHAIN ANALYSIS 
    5.8 REGULATORY LANDSCAPE 
           5.8.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
           5.8.2 REGULATIONS: MODELOPS
                    5.8.2.1 North America
                               5.8.2.1.1 US
                               5.8.2.1.2 Canada
                    5.8.2.2 Europe
                    5.8.2.3 Asia Pacific
                               5.8.2.3.1 Singapore
                               5.8.2.3.2 China
                               5.8.2.3.3 India
                               5.8.2.3.4 Japan
                    5.8.2.4 Middle East & Africa
                               5.8.2.4.1 UAE
                               5.8.2.4.2 KSA
                               5.8.2.4.3 South Africa
                    5.8.2.5 Latin America
                               5.8.2.5.1 Brazil
                               5.8.2.5.2 Mexico
    5.9 PATENT ANALYSIS 
           5.9.1 METHODOLOGY
           5.9.2 PATENTS FILED, BY DOCUMENT TYPE
           5.9.3 INNOVATIONS AND PATENT APPLICATIONS
                    5.9.3.1 Patent applicants
    5.10 KEY CONFERENCES AND EVENTS, 2024-2025 
    5.11 PORTER’S FIVE FORCES ANALYSIS 
           5.11.1 THREAT FROM NEW ENTRANTS
           5.11.2 THREAT OF SUBSTITUTES
           5.11.3 BARGAINING POWER OF SUPPLIERS
           5.11.4 BARGAINING POWER OF BUYERS
           5.11.5 INTENSITY OF COMPETITIVE RIVALRY
    5.12 PRICING ANALYSIS 
           5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
           5.12.2 INDICATIVE PRICING ANALYSIS, BY OFFERING
    5.13 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 
    5.14 KEY STAKEHOLDERS AND BUYING CRITERIA 
           5.14.1 KEY STAKEHOLDERS IN BUYING PROCESS
           5.14.2 BUYING CRITERIA
    5.15 INVESTMENT AND FUNDING SCENARIO 
    5.16 MODELOPS VS. MLOPS 
    5.17 MODELOPS BEST PRACTICES 
 
6 MODELOPS MARKET, BY OFFERING (Page No. - 92)
    6.1 INTRODUCTION 
           6.1.1 OFFERING: MODELOPS MARKET DRIVERS
    6.2 PLATFORMS 
           6.2.1 OPTIMIZING MACHINE LEARNING MODEL LIFECYCLE MANAGEMENT WITH MODELOPS PLATFORMS
           6.2.2 TYPE
                    6.2.2.1 Development & experimentation platforms
                    6.2.2.2 Monitoring & observability tools
                    6.2.2.3 Automated machine learning (AutoML) platforms
                    6.2.2.4 Performance tracking & management platforms
                    6.2.2.5 Model explainability & interpretability tools
                    6.2.2.6 Serving & deployment tools
                    6.2.2.7 Others
           6.2.3 DEPLOYMENT MODE
                    6.2.3.1 Cloud
                    6.2.3.2 On-premises
    6.3 SERVICES 
           6.3.1 ELEVATING DATA INSIGHTS WITH MODELOPS SERVICES
           6.3.2 CONSULTING
           6.3.3 DEPLOYMENT & INTEGRATION
           6.3.4 SUPPORT & MAINTENANCE
 
7 MODELOPS MARKET, BY MODEL TYPE (Page No. - 112)
    7.1 INTRODUCTION 
           7.1.1 MODEL TYPE: MODELOPS MARKET DRIVERS
    7.2 ML MODELS 
           7.2.1 SEGMENTING, FORECASTING, AND OPTIMIZING MODELOPS FOR COMPETITIVE ADVANTAGE
    7.3 GRAPH-BASED MODELS 
           7.3.1 GRAPH-BASED MODELS ENHANCE PREDICTIONS AND DECISION-MAKING IN MODELOPS
    7.4 RULE & HEURISTIC MODELS 
           7.4.1 OPTIMIZING MODELOPS WITH RULE-BASED, HEURISTIC, AND HYBRID MODELS
    7.5 LINGUISTIC MODELS 
           7.5.1 OPTIMIZING LINGUISTIC MODELS FOR EFFICIENT NLP DEPLOYMENT AND GOVERNANCE
    7.6 AGENT-BASED MODELS 
           7.6.1 ENHANCING STRATEGIC DECISION-MAKING THROUGH ADVANCED AGENT-BASED MODEL SIMULATION
    7.7 BRING YOUR OWN MODELS 
           7.7.1 MAXIMIZING OPERATIONAL EFFICIENCY THROUGH SEAMLESS INTEGRATION OF DIVERSE AI MODELS
    7.8 OTHER MODEL TYPES 
 
8 MODELOPS MARKET, BY APPLICATION (Page No. - 122)
    8.1 INTRODUCTION 
           8.1.1 APPLICATION: MODELOPS MARKET DRIVERS
    8.2 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT 
           8.2.1 IMPLEMENTATION OF CI/CD FOR ACCELERATED DEPLOYMENT OF MACHINE LEARNING MODELS IN MODELOPS
    8.3 MONITORING & ALERTING 
           8.3.1 ENHANCING MODELOPS WITH RELIABLE MONITORING & ALERTING SERVICES
    8.4 DASHBOARD & REPORTING 
           8.4.1 DASHBOARD AND REPORTING ENHANCE OPERATIONAL PROCESSES SURROUNDING MACHINE LEARNING MODELS
    8.5 MODEL LIFECYCLE MANAGEMENT 
           8.5.1 MAXIMIZING AI VALUE THROUGH EFFECTIVE MODEL LIFECYCLE MANAGEMENT
    8.6 GOVERNANCE, RISK, & COMPLIANCE 
           8.6.1 IMPLEMENTATION OF ROBUST GOVERNANCE, RISK, AND COMPLIANCE (GRC) FRAMEWORK IN MODELOPS FOR EFFECTIVE AI MODEL MANAGEMENT
    8.7 PARALLELIZATION & DISTRIBUTED COMPUTING 
           8.7.1 EMPOWERING AI/ML SCALABILITY WITH PARALLELIZATION AND DISTRIBUTED COMPUTING IN MODELOPS
    8.8 BATCH SCORING 
           8.8.1 ENHANCING DATA-DRIVEN DECISION-MAKING WITH BATCH SCORING IN MODELOPS
    8.9 OTHER APPLICATIONS 
 
9 MODELOPS MARKET, BY VERTICAL (Page No. - 133)
    9.1 INTRODUCTION 
           9.1.1 VERTICAL: MODELOPS MARKET DRIVERS
    9.2 BFSI 
           9.2.1 OPTIMIZING MODELOPS FOR BFSI SECTOR ADVANCEMENTS
    9.3 TELECOMMUNICATIONS 
           9.3.1 IMPLEMENTING MODELOPS FOR ENHANCED TELECOMMUNICATION EFFICIENCY
    9.4 RETAIL & ECOMMERCE 
           9.4.1 STREAMLINING AI AND ML DEPLOYMENT TO REVOLUTIONIZE RETAIL AND ECOMMERCE OPERATIONS FOR ENHANCED EFFICIENCY AND CUSTOMER EXPERIENCE
    9.5 HEALTHCARE & LIFE SCIENCES 
           9.5.1 ENHANCING PATIENT OUTCOMES AND MEDICAL INNOVATION THROUGH MODELOPS IN HEALTHCARE AND LIFE SCIENCES
    9.6 GOVERNMENT & DEFENSE 
           9.6.1 GOVERNMENTS USE MODELOPS TO APPLY REAL-TIME ANALYTICS IN MISSION-CRITICAL SCENARIOS
    9.7 IT/ITES 
           9.7.1 IMPLEMENTING MODELOPS FOR EFFICIENT AI/ML LIFECYCLE MANAGEMENT IN IT/ITES
    9.8 ENERGY & UTILITIES 
           9.8.1 IMPLEMENTING MODELOPS FOR ENERGY AND UTILITIES OPTIMIZATION
    9.9 MANUFACTURING 
           9.9.1 DEPLOYING MODELOPS FOR ENHANCED MANUFACTURING EFFICIENCY
    9.10 TRANSPORTATION & LOGISTICS 
           9.10.1 ENHANCING EFFICIENCY AND SAFETY THROUGH MODELOPS IN TRANSPORTATION AND LOGISTICS
    9.11 OTHER VERTICALS 
 
10 MODELOPS MARKET, BY REGION (Page No. - 150)
     10.1 INTRODUCTION 
     10.2 NORTH AMERICA 
             10.2.1 NORTH AMERICA: MODELOPS MARKET DRIVERS
             10.2.2 NORTH AMERICA: RECESSION IMPACT
             10.2.3 US
                       10.2.3.1 Widespread adoption of AI and ML technologies across industries to drive market
             10.2.4 CANADA
                       10.2.4.1 Rising demand for AI and ML solutions in various sectors to drive market
     10.3 EUROPE 
             10.3.1 EUROPE: MODELOPS MARKET DRIVERS
             10.3.2 EUROPE: RECESSION IMPACT
             10.3.3 UK
                       10.3.3.1 Increasing AI adoption across industries to drive market
             10.3.4 GERMANY
                       10.3.4.1 Increasing adoption of AI and ML technologies to drive market
             10.3.5 FRANCE
                       10.3.5.1 Rising focus on operationalizing AI and ML models to drive market
             10.3.6 ITALY
                       10.3.6.1 Growing integration of AI and ML across diverse sectors to drive market
             10.3.7 SPAIN
                       10.3.7.1 Increasing reliance on data-driven decision-making across industries to drive market
             10.3.8 REST OF EUROPE
     10.4 ASIA PACIFIC 
             10.4.1 ASIA PACIFIC: MODELOPS MARKET DRIVERS
             10.4.2 ASIA PACIFIC: RECESSION IMPACT
             10.4.3 CHINA
                       10.4.3.1 Rising focus on operationalizing AI models and enhancing business outcomes to drive market
             10.4.4 JAPAN
                       10.4.4.1 Increasing adoption of AI and ML models in various industries to drive market
             10.4.5 INDIA
                       10.4.5.1 Rising adoption of AI technologies across sectors to drive market
             10.4.6 SOUTH KOREA
                       10.4.6.1 Increasing adoption of AI across sectors to drive market
             10.4.7 AUSTRALIA & NEW ZEALAND
                       10.4.7.1 Growing emphasis on integrating AI solutions to enhance operational efficiency to drive market
             10.4.8 REST OF ASIA PACIFIC
     10.5 MIDDLE EAST & AFRICA 
             10.5.1 MIDDLE EAST & AFRICA: MODELOPS MARKET DRIVERS
             10.5.2 MIDDLE EAST & AFRICA: RECESSION IMPACT
             10.5.3 UAE
                       10.5.3.1 Government initiatives toward building knowledge-based economy to drive market
             10.5.4 KSA
                       10.5.4.1 Growing emphasis on digital transformation and AI integration across sectors to drive market
             10.5.5 QATAR
                       10.5.5.1 Rising adoption of AI and ML technologies across sectors to drive market
             10.5.6 EGYPT
                       10.5.6.1 Increasing investments by companies to operationalize AI and ML models to drive market
             10.5.7 SOUTH AFRICA
                       10.5.7.1 Growing adoption of AI and machine learning models in various sectors to drive market
             10.5.8 REST OF MIDDLE EAST & AFRICA
     10.6 LATIN AMERICA 
             10.6.1 LATIN AMERICA: MODELOPS MARKET DRIVERS
             10.6.2 LATIN AMERICA: RECESSION IMPACT
             10.6.3 BRAZIL
                       10.6.3.1 Technological advancements and regulatory compliance to drive market
             10.6.4 MEXICO
                       10.6.4.1 Increasing digital transformation efforts across industries to drive market
             10.6.5 ARGENTINA
                       10.6.5.1 Increasing adoption of machine learning and AI technologies in various sectors to drive market
             10.6.6 REST OF LATIN AMERICA
 
11 COMPETITIVE LANDSCAPE (Page No. - 207)
     11.1 OVERVIEW 
     11.2 STRATEGIES ADOPTED BY KEY PLAYERS 
     11.3 REVENUE ANALYSIS 
     11.4 MARKET SHARE ANALYSIS 
             11.4.1 MARKET RANKING ANALYSIS
     11.5 PRODUCT COMPARATIVE ANALYSIS 
     11.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 
             11.6.1 STARS
             11.6.2 EMERGING LEADERS
             11.6.3 PERVASIVE PLAYERS
             11.6.4 PARTICIPANTS
             11.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
                       11.6.5.1 Company footprint
                       11.6.5.2 Offering footprint
                       11.6.5.3 Application footprint
                       11.6.5.4 Regional footprint
                       11.6.5.5 Vertical footprint
     11.7 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023 
             11.7.1 PROGRESSIVE COMPANIES
             11.7.2 RESPONSIVE COMPANIES
             11.7.3 DYNAMIC COMPANIES
             11.7.4 STARTING BLOCKS
             11.7.5 COMPETITIVE BENCHMARKING: START-UPS/SMES, 2023
     11.8 COMPETITIVE SCENARIOS AND TRENDS 
             11.8.1 PRODUCT LAUNCHES & ENHANCEMENTS
             11.8.2 DEALS
     11.9 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS 
 
12 COMPANY PROFILES (Page No. - 228)
     12.1 INTRODUCTION 
     12.2 KEY PLAYERS 
(Business Overview, Products/Solutions/Services offered, Recent Developments, MnM View)*
             12.2.1 IBM
             12.2.2 GOOGLE
             12.2.3 SAS INSTITUTE
             12.2.4 AWS
             12.2.5 ORACLE
             12.2.6 TERADATA
             12.2.7 VERITONE
             12.2.8 ALTAIR
             12.2.9 C3.AI
               12.2.10 PALANTIR
               12.2.11 TIBCO SOFTWARE
               12.2.12 DOMINO DATA LAB
               12.2.13 DATABRICKS
               12.2.14 GIGGSO
               12.2.15 MODELOP
     12.3 OTHER PLAYERS 
             12.3.1 VERTA
             12.3.2 COMET ML
             12.3.3 SUPERWISE
             12.3.4 EVIDENTLY AI
             12.3.5 MINITAB
             12.3.6 SELDON
             12.3.7 INNOMINDS
             12.3.8 DATATRON
             12.3.9 ARTHUR AI
               12.3.10 WEIGHTS & BIASES
               12.3.11 XENONSTACK
               12.3.12 CNVRG.IO
               12.3.13 DATAKITCHEN
               12.3.14 HAISTEN AI
               12.3.15 SPARKLING LOGIC
               12.3.16 LEEWAYHERTZ
*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 (Page No. - 285)
     13.1 INTRODUCTION 
     13.2 MLOPS 
             13.2.1 MARKET DEFINITION
             13.2.2 MARKET OVERVIEW
                       13.2.2.1 MLOps market, by component
                       13.2.2.2 MLOps market, by deployment mode
                       13.2.2.3 MLOps market, by organization size
                       13.2.2.4 MLOps market, by vertical
                       13.2.2.5 MLOps market, by region
     13.3 ARTIFICIAL INTELLIGENCE (AI) MARKET 
             13.3.1 MARKET DEFINITION
             13.3.2 MARKET OVERVIEW
                       13.3.2.1 Artificial intelligence (AI) market, by offering
                       13.3.2.2 Artificial intelligence (AI) market, by hardware
                       13.3.2.3 Artificial intelligence (AI) market, by software
                       13.3.2.4 Artificial intelligence (AI) market, by services
                       13.3.2.5 Artificial intelligence (AI) market, by technology
                       13.3.2.6 Artificial intelligence (AI) market, by business function
                       13.3.2.7 Artificial intelligence (AI) market, by vertical
                       13.3.2.8 Artificial intelligence (AI) market, by region
 
14 APPENDIX (Page No. - 300)
     14.1 DISCUSSION GUIDE 
     14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 
     14.3 CUSTOMIZATION OPTIONS 
     14.4 RELATED REPORTS 
     14.5 AUTHOR DETAILS 
 
 
LIST OF TABLES (316 Tables) 
 
TABLE 1 USD EXCHANGE RATE, 2020–2023
TABLE 2 PRIMARY INTERVIEWS
TABLE 3 FACTOR ANALYSIS
TABLE 4 MODELOPS MARKET AND GROWTH RATE, 2019–2023 (USD MILLION, Y-O-Y%)
TABLE 5 MODELOPS MARKET AND GROWTH RATE, 2024–2029 (USD MILLION, Y-O-Y%)
TABLE 6 MODELOPS MARKET: ECOSYSTEM
TABLE 7 NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 8 EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 9 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 10 MIDDLE EAST & AFRICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 11 LATIN AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 12 PATENTS FILED, BY DOCUMENT TYPE, 2020–2024
TABLE 13 PATENT OWNERS IN MODELOPS MARKET, 2020–2024
TABLE 14 MODELOPS MARKET: LIST OF PATENTS GRANTED, 2023–2024
TABLE 15 MODELOPS MARKET: DETAILED LIST OF CONFERENCES AND EVENTS, 2024–2025
TABLE 16 MODELOPS MARKET: IMPACT OF PORTER’S FIVE FORCES
TABLE 17 AVERAGE SELLING PRICE OF KEY PLAYERS, BY APPLICATIONS
TABLE 18 INDICATIVE PRICING OF MODELOPS BY OFFERINGS
TABLE 19 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP 3 VERTICALS
TABLE 20 KEY BUYING CRITERIA FOR TOP 3 VERTICALS
TABLE 21 MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 22 MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 23 MODELOPS MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 24 MODELOPS MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 25 MODELOPS MARKET FOR PLATFORMS, BY TYPE, 2019–2023 (USD MILLION)
TABLE 26 MODELOPS MARKET FOR PLATFORMS, BY TYPE, 2024–2029 (USD MILLION)
TABLE 27 DEVELOPMENT & EXPERIMENTATION PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 28 DEVELOPMENT & EXPERIMENTATION PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 29 MONITORING & OBSERVABILITY TOOLS, BY REGION, 2019–2023 (USD MILLION)
TABLE 30 MONITORING & OBSERVABILITY TOOLS, BY REGION, 2024–2029 (USD MILLION)
TABLE 31 AUTOMATED MACHINE LEARNING (AUTOML) PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 32 AUTOMATED MACHINE LEARNING (AUTOML) PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 33 PERFORMANCE TRACKING & MANAGEMENT PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 34 PERFORMANCE TRACKING & MANAGEMENT PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 35 MODEL EXPLAINABILITY & INTERPRETABILITY TOOLS, BY REGION, 2019–2023 (USD MILLION)
TABLE 36 MODEL EXPLAINABILITY & INTERPRETABILITY TOOLS, BY REGION, 2024–2029 (USD MILLION)
TABLE 37 SERVING & DEPLOYMENT TOOLS, BY REGION, 2019–2023 (USD MILLION)
TABLE 38 SERVING & DEPLOYMENT TOOLS, BY REGION, 2024–2029 (USD MILLION)
TABLE 39 MODELOPS MARKET FOR PLATFORMS, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 40 MODELOPS MARKET FOR PLATFORMS, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 41 CLOUD: MODELOPS MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 42 CLOUD: MODELOPS MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 43 ON-PREMISES: MODELOPS MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
TABLE 44 ON-PREMISES: MODELOPS MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
TABLE 45 MODELOPS MARKET FOR SERVICES, BY TYPE, 2019–2023 (USD MILLION)
TABLE 46 MODELOPS MARKET FOR SERVICES, BY TYPE, 2024–2029 (USD MILLION)
TABLE 47 MODELOPS MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
TABLE 48 MODELOPS MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
TABLE 49 CONSULTING: MODELOPS MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
TABLE 50 CONSULTING: MODELOPS MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
TABLE 51 DEPLOYMENT & INTEGRATION: MODELOPS MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
TABLE 52 DEPLOYMENT & INTEGRATION: MODELOPS MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
TABLE 53 SUPPORT & MAINTENANCE: MODELOPS MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
TABLE 54 SUPPORT & MAINTENANCE: MODELOPS MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
TABLE 55 MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 56 MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 57 ML MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 58 ML MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 59 GRAPH-BASED MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 60 GRAPH-BASED MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 61 RULE & HEURISTIC MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 62 RULE & HEURISTIC MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 63 LINGUISTIC MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 64 LINGUISTIC MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 65 AGENT-BASED MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 66 AGENT-BASED MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 67 BRING YOUR OWN MODELS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 68 BRING YOUR OWN MODELS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 69 OTHER MODEL TYPES: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 70 OTHER MODEL TYPES: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 71 MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 72 MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 73 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 74 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 75 MONITORING & ALERTING: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 76 MONITORING & ALERTING: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 77 DASHBOARD & REPORTING: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 78 DASHBOARD & REPORTING: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 79 MODEL LIFECYCLE MANAGEMENT: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 80 MODEL LIFECYCLE MANAGEMENT: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 81 GOVERNANCE, RISK, & COMPLIANCE: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 82 GOVERNANCE, RISK, & COMPLIANCE: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 83 PARALLELIZATION & DISTRIBUTED COMPUTING: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 84 PARALLELIZATION & DISTRIBUTED COMPUTING: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 85 BATCH SCORING: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 86 BATCH SCORING: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 87 OTHER APPLICATIONS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 88 OTHER APPLICATIONS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 89 MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 90 MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 91 BANKING, FINANCIAL SERVICES, AND INSURANCE: USE CASES
TABLE 92 BFSI: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 93 BFSI: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 94 TELECOMMUNICATIONS: USE CASES
TABLE 95 TELECOMMUNICATIONS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 96 TELECOMMUNICATIONS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 97 RETAIL & ECOMMERCE: USE CASES
TABLE 98 RETAIL & ECOMMERCE: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 99 RETAIL & ECOMMERCE: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 100 HEALTHCARE & LIFE SCIENCES: USE CASES
TABLE 101 HEALTHCARE & LIFE SCIENCES: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 102 HEALTHCARE & LIFE SCIENCES: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 103 GOVERNMENT & DEFENSE: USE CASES
TABLE 104 GOVERNMENT & DEFENSE: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 105 GOVERNMENT & DEFENSE: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 106 IT/ITES: USE CASES
TABLE 107 IT/ITES: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 108 IT/ITES: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 109 ENERGY & UTILITIES: USE CASES
TABLE 110 ENERGY & UTILITIES: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 111 ENERGY & UTILITIES: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 112 MANUFACTURING: USE CASES
TABLE 113 MANUFACTURING: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 114 MANUFACTURING: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 115 TRANSPORTATION & LOGISTICS: USE CASES
TABLE 116 TRANSPORTATION & LOGISTICS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 117 TRANSPORTATION & LOGISTICS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 118 OTHER VERTICALS: USE CASES
TABLE 119 OTHER VERTICALS: MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 120 OTHER VERTICALS: MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 121 MODELOPS MARKET, BY REGION, 2019–2023 (USD MILLION)
TABLE 122 MODELOPS MARKET, BY REGION, 2024–2029 (USD MILLION)
TABLE 123 NORTH AMERICA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 124 NORTH AMERICA: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 125 NORTH AMERICA: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 126 NORTH AMERICA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 127 NORTH AMERICA: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 128 NORTH AMERICA: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 129 NORTH AMERICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 130 NORTH AMERICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 131 NORTH AMERICA: MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 132 NORTH AMERICA: MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 133 NORTH AMERICA: MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 134 NORTH AMERICA: MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 135 NORTH AMERICA: MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 136 NORTH AMERICA: MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 137 NORTH AMERICA: MODELOPS MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
TABLE 138 NORTH AMERICA: MODELOPS MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
TABLE 139 US: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 140 US: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 141 US: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 142 US: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 143 US: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 144 US: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 145 US: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 146 US: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 147 CANADA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 148 CANADA: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 149 CANADA: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 150 CANADA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 151 CANADA: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 152 CANADA: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 153 CANADA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 154 CANADA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 155 EUROPE: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 156 EUROPE: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 157 EUROPE: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 158 EUROPE: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 159 EUROPE: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 160 EUROPE: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 161 EUROPE: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 162 EUROPE: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 163 EUROPE: MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 164 EUROPE: MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 165 EUROPE: MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 166 EUROPE: MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 167 EUROPE: MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 168 EUROPE: MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 169 EUROPE: MODELOPS MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
TABLE 170 EUROPE: MODELOPS MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
TABLE 171 UK: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 172 UK: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 173 UK: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 174 UK: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 175 UK: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 176 UK: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 177 UK: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 178 UK: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 179 ASIA PACIFIC: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 180 ASIA PACIFIC: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 181 ASIA PACIFIC: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 182 ASIA PACIFIC: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 183 ASIA PACIFIC: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 184 ASIA PACIFIC: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 185 ASIA PACIFIC: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 186 ASIA PACIFIC: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 187 ASIA PACIFIC: MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 188 ASIA PACIFIC: MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 189 ASIA PACIFIC: MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 190 ASIA PACIFIC: MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 191 ASIA PACIFIC: MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 192 ASIA PACIFIC: MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 193 ASIA PACIFIC: MODELOPS MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
TABLE 194 ASIA PACIFIC: MODELOPS MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
TABLE 195 CHINA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 196 CHINA: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 197 CHINA: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 198 CHINA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 199 CHINA: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 200 CHINA: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 201 CHINA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 202 CHINA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 203 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 204 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 205 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 206 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 207 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 208 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 209 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 210 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 211 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 212 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 213 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 214 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 215 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 216 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 217 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
TABLE 218 MIDDLE EAST & AFRICA: MODELOPS MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
TABLE 219 LATIN AMERICA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
TABLE 220 LATIN AMERICA: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
TABLE 221 LATIN AMERICA: MODELOPS MARKET, BY TYPE, 2019–2023 (USD MILLION)
TABLE 222 LATIN AMERICA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
TABLE 223 LATIN AMERICA: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
TABLE 224 LATIN AMERICA: MODELOPS MARKET, BY SERVICE, 2024–2029 (USD MILLION)
TABLE 225 LATIN AMERICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
TABLE 226 LATIN AMERICA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
TABLE 227 LATIN AMERICA: MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
TABLE 228 LATIN AMERICA: MODELOPS MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
TABLE 229 LATIN AMERICA: MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
TABLE 230 LATIN AMERICA: MODELOPS MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
TABLE 231 LATIN AMERICA: MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
TABLE 232 LATIN AMERICA: MODELOPS MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
TABLE 233 LATIN AMERICA: MODELOPS MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
TABLE 234 LATIN AMERICA: MODELOPS MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
TABLE 235 OVERVIEW OF STRATEGIES ADOPTED BY KEY MODELOPS VENDORS
TABLE 236 MODELOPS MARKET: DEGREE OF COMPETITION
TABLE 237 MODELOPS MARKET: OFFERING FOOTPRINT (12 COMPANIES)
TABLE 238 MODELOPS MARKET: APPLICATION FOOTPRINT (12 COMPANIES)
TABLE 239 MODELOPS MARKET: REGIONAL FOOTPRINT (12 COMPANIES)
TABLE 240 MODELOPS MARKET: VERTICAL FOOTPRINT (12 COMPANIES)
TABLE 241 MODELOPS MARKET: DETAILED LIST OF KEY START-UPS/SMES
TABLE 242 MODELOPS MARKET: COMPETITIVE BENCHMARKING OF KEY START-UPS/SMES
TABLE 243 MODELOPS MARKET: PRODUCT LAUNCHES & ENHANCEMENTS, JANUARY 2022–MAY 2024
TABLE 244 MODELOPS MARKET: DEALS, JANUARY 2022–MAY 2024
TABLE 245 IBM: COMPANY OVERVIEW
TABLE 246 IBM: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 247 IBM: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 248 IBM: DEALS
TABLE 249 GOOGLE: COMPANY OVERVIEW
TABLE 250 GOOGLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 251 GOOGLE: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 252 GOOGLE: DEALS
TABLE 253 SAS INSTITUTE: COMPANY OVERVIEW
TABLE 254 SAS INSTITUTE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 255 SAS INSTITUTE: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 256 SAS INSTITUTE: DEALS
TABLE 257 AWS: COMPANY OVERVIEW
TABLE 258 AWS: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 259 AWS: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 260 AWS: DEALS
TABLE 261 ORACLE: COMPANY OVERVIEW
TABLE 262 ORACLE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 263 ORACLE: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 264 ORACLE: DEALS
TABLE 265 TERADATA: COMPANY OVERVIEW
TABLE 266 TERADATA: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 267 TERADATA: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 268 TERADATA: DEALS
TABLE 269 VERITONE: COMPANY OVERVIEW
TABLE 270 VERITONE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 271 VERITONE: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 272 VERITONE: DEALS
TABLE 273 ALTAIR: COMPANY OVERVIEW
TABLE 274 ALTAIR: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 275 ALTAIR: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 276 ALTAIR: DEALS
TABLE 277 C3.AI: COMPANY OVERVIEW
TABLE 278 C3.AI: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 279 C3.AI: DEALS
TABLE 280 PALANTIR: COMPANY OVERVIEW
TABLE 281 PALANTIR: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 282 PALANTIR: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 283 PALANTIR: DEALS
TABLE 284 TIBCO SOFTWARE: COMPANY OVERVIEW
TABLE 285 TIBCO SOFTWARE: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 286 TIBCO SOFTWARE: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 287 DOMINO DATA LAB: COMPANY OVERVIEW
TABLE 288 DOMINO DATA LAB: PRODUCTS/SOLUTIONS/SERVICES OFFERED
TABLE 289 DOMINO DATA LAB: PRODUCT LAUNCHES & ENHANCEMENTS
TABLE 290 DOMINO DATA LAB: DEALS
TABLE 291 MLOPS MARKET, BY COMPONENT, 2018–2021 (USD MILLION)
TABLE 292 MLOPS MARKET, BY COMPONENT, 2022–2027 (USD MILLION)
TABLE 293 MLOPS MARKET, BY DEPLOYMENT MODE, 2018–2021 (USD MILLION)
TABLE 294 MLOPS MARKET, BY DEPLOYMENT MODE, 2022–2027 (USD MILLION)
TABLE 295 MLOPS MARKET, BY ORGANIZATION SIZE, 2018–2021 (USD MILLION)
TABLE 296 MLOPS MARKET, BY ORGANIZATION SIZE, 2022–2027 (USD MILLION)
TABLE 297 MLOPS MARKET, BY VERTICAL, 2018–2021 (USD MILLION)
TABLE 298 MLOPS MARKET, BY VERTICAL, 2022–2027 (USD MILLION)
TABLE 299 MLOPS MARKET, BY REGION, 2018–2021 (USD MILLION)
TABLE 300 MLOPS MARKET, BY REGION, 2022–2027 (USD MILLION)
TABLE 301 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2019–2023 (USD BILLION)
TABLE 302 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2024–2030 (USD BILLION)
TABLE 303 ARTIFICIAL INTELLIGENCE MARKET, BY HARDWARE, 2019–2023 (USD BILLION)
TABLE 304 ARTIFICIAL INTELLIGENCE MARKET, BY HARDWARE, 2024–2030 (USD BILLION)
TABLE 305 SOFTWARE: ARTIFICIAL INTELLIGENCE MARKET, BY TYPE, 2019–2023 (USD BILLION)
TABLE 306 SOFTWARE: ARTIFICIAL INTELLIGENCE MARKET, BY TYPE, 2024–2030 (USD BILLION)
TABLE 307 ARTIFICIAL INTELLIGENCE MARKET, BY SERVICES, 2019–2023 (USD BILLION)
TABLE 308 ARTIFICIAL INTELLIGENCE MARKET, BY SERVICES, 2024–2030 (USD BILLION)
TABLE 309 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2019–2023 (USD BILLION)
TABLE 310 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2024–2030 (USD BILLION)
TABLE 311 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2019–2023 (USD BILLION)
TABLE 312 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2024–2030 (USD BILLION)
TABLE 313 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2019–2023 (USD BILLION)
TABLE 314 ARTIFICIAL INTELLIGENCE MARKET, BY VERTICAL, 2024–2030 (USD BILLION)
TABLE 315 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2019–2023 (USD BILLION)
TABLE 316 ARTIFICIAL INTELLIGENCE MARKET, BY REGION, 2024–2030 (USD BILLION)
 
  
LIST OF FIGURES (61 Figures)
 
FIGURE 1 MODELOPS MARKET: RESEARCH DESIGN
FIGURE 2 DATA TRIANGULATION
FIGURE 3 MODELOPS MARKET: TOP-DOWN AND BOTTOM-UP APPROACHES
FIGURE 4 APPROACH 1 (SUPPLY-SIDE): REVENUE FROM VENDORS OF MODELOPS PLATFORMS/SERVICES
FIGURE 5 APPROACH 2 (BOTTOM-UP, SUPPLY-SIDE): COLLECTIVE REVENUE FROM ALL PLATFORMS/SERVICES OF MODELOPS
FIGURE 6 APPROACH 3 (BOTTOM-UP, SUPPLY-SIDE): MARKET ESTIMATION FROM ALL PLATFORMS/SERVICES AND CORRESPONDING SOURCES
FIGURE 7 APPROACH 4 (BOTTOM-UP, DEMAND-SIDE): SHARE OF MODELOPS MARKET THROUGH OVERALL SPENDING
FIGURE 8 PLATFORMS SEGMENT TO DOMINATE MARKET IN 2024
FIGURE 9 MONITORING & OBSERVABILITY TOOLS SEGMENT TO LEAD MARKET IN 2024
FIGURE 10 CLOUD SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
FIGURE 11 CONSULTING SEGMENT TO LEAD MARKET IN 2024
FIGURE 12 ML MODELS SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
FIGURE 13 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT SEGMENT TO LEAD MARKET IN 2024
FIGURE 14 BFSI SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
FIGURE 15 NORTH AMERICA TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
FIGURE 16 GROWING ADOPTION OF ML & AI MODELS TO MAXIMIZE MODELOPS POTENTIAL TO DRIVE MARKET
FIGURE 17 MODELOPS MARKET SIZE AND Y-O-Y GROWTH RATE
FIGURE 18 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT SEGMENT TO ACCOUNT FOR LARGEST MARKET SHARE FROM 2024 TO 2029
FIGURE 19 ML MODELS AND CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT SEGMENTS TO ACCOUNT FOR LARGEST MARKET SHARE IN 2024
FIGURE 20 NORTH AMERICA TO DOMINATE MARKET IN 2024
FIGURE 21 MODELOPS MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
FIGURE 22 EVOLUTION OF MODELOPS MARKET
FIGURE 23 KEY PLAYERS IN MODELOPS MARKET ECOSYSTEM
FIGURE 24 MODELOPS MARKET: SUPPLY CHAIN ANALYSIS
FIGURE 25 NUMBER OF PATENTS GRANTED TO VENDORS IN LAST 5 YEARS
FIGURE 26 PATENT APPLICANTS IN LAST 5 YEARS
FIGURE 27 REGIONAL ANALYSIS OF PATENTS GRANTED, 2020–2024
FIGURE 28 PORTER’S FIVE FORCES ANALYSIS
FIGURE 29 AVERAGE SELLING PRICE OF KEY PLAYERS, BY APPLICATIONS
FIGURE 30 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
FIGURE 31 INFLUENCE OF STAKEHOLDERS ON BUYING PROCESS FOR TOP 3 VERTICALS
FIGURE 32 KEY BUYING CRITERIA FOR TOP 3 VERTICALS
FIGURE 33 MODELOPS MARKET: INVESTMENT AND FUNDING SCENARIO (USD MILLION AND NUMBER OF FUNDING ROUNDS)
FIGURE 34 PLATFORMS SEGMENT TO LEAD MARKET DURING FORECAST PERIOD
FIGURE 35 MONITORING & OBSERVABILITY TOOLS SEGMENT TO LEAD MARKET DURING FORECAST PERIOD
FIGURE 36 CLOUD SEGMENT TO LEAD MARKET DURING FORECAST PERIOD
FIGURE 37 CONSULTING SEGMENT TO LEAD MARKET DURING FORECAST PERIOD
FIGURE 38 GRAPH-BASED MODELS SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 39 MONITORING & ALERTING SEGMENT TO REGISTER HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 40 BFSI SEGMENT TO DOMINATE MARKET FROM 2024 TO 2029
FIGURE 41 NORTH AMERICA TO LEAD MARKET DURING FORECAST PERIOD
FIGURE 42 INDIA TO WITNESS FASTEST GROWTH DURING FORECAST PERIOD
FIGURE 43 NORTH AMERICA: MODELOPS MARKET SNAPSHOT
FIGURE 44 ASIA PACIFIC: MODELOPS MARKET SNAPSHOT
FIGURE 45 TOP 5 PLAYERS DOMINATED MARKET IN LAST 5 YEARS
FIGURE 46 MARKET SHARE ANALYSIS FOR KEY PLAYERS, 2023
FIGURE 47 PRODUCT COMPARATIVE ANALYSIS
FIGURE 48 MODELOPS MARKET: COMPANY EVALUATION MATRIX (KEY PLAYERS), 2023
FIGURE 49 MODELOPS MARKET: COMPANY FOOTPRINT (12 COMPANIES)
FIGURE 50 MODELOPS MARKET: COMPANY EVALUATION MATRIX (START-UPS/SMES), 2023
FIGURE 51 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
FIGURE 52 YEAR-TO-DATE (YTD) PRICE TOTAL RETURN AND FIVE-YEAR STOCK BETA OF KEY VENDORS
FIGURE 53 IBM: COMPANY SNAPSHOT
FIGURE 54 GOOGLE: COMPANY SNAPSHOT
FIGURE 55 AWS: COMPANY SNAPSHOT
FIGURE 56 ORACLE: COMPANY SNAPSHOT
FIGURE 57 TERADATA: COMPANY SNAPSHOT
FIGURE 58 VERITONE: COMPANY SNAPSHOT
FIGURE 59 ALTAIR: COMPANY SNAPSHOT
FIGURE 60 C3.AI: COMPANY SNAPSHOT
FIGURE 61 PALANTIR: COMPANY SNAPSHOT

The research study for the ModelOps Market involved extensive secondary sources, directories, and several journals. Primary sources were mainly industry experts from the core and related industries, preferred modelOps platforms providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews with primary respondents, including key industry participants and subject matter experts, were conducted to obtain and verify critical qualitative and quantitative information and assess the market’s prospects.

Secondary Research

The market size of companies offering modelOps platforms and services was determined based on secondary data from paid and unpaid sources. It was also arrived at by analyzing the product portfolios of major companies and rating the companies based on their performance and quality.

In the secondary research process, various sources were referred to for identifying and collecting information for this study. Secondary sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as journals, government websites, blogs, and vendor websites. Additionally, modelOps spending of various countries was extracted from the respective sources. Secondary research was mainly used to obtain key information related to the industry’s value chain and supply chain to identify key players based on platforms, services, market classification, and segmentation according to offerings of major players, industry trends related to offering, data type, data processing, vertical, and region, and key developments from both market- and technology-oriented perspectives

Primary Research

In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and modelOps expertise; related key executives from modelOps platform vendors, System Integrators (SIs), professional service providers, and industry associations; and key opinion leaders.

Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from platforms and services, market breakups, market size estimations, market forecasts, and data triangulation. Primary research also helped understand various trends related to technologies, applications, deployments, and regions. Stakeholders from the demand side, such as Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs), and end users using modelOps, were interviewed to understand the buyer’s perspective on suppliers, products, service providers, and their current usage of modelOps platform and services which would impact the overall ModelOps Market.

The following is the breakup of primary profiles:

ModelOps Market Market Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Multiple approaches were adopted for estimating and forecasting the ModelOps Market. The first approach estimates market size by summating companies’ revenue generated by selling platforms and services.

Market Size Estimation Methodology-Top-down approach

In the top-down approach, an exhaustive list of all the vendors offering platforms and services in the ModelOps Market was prepared. The revenue contribution of the market vendors was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor’s offerings were evaluated based on the breadth of offering, data type, data processing, vertical, and region. The aggregate of all the companies’ revenue was extrapolated to reach the overall market size. Each subsegment was studied and analyzed for its global market size and regional penetration. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets repository for validation.

Market Size Estimation Methodology-Bottom-up approach

The bottom-up approach identified the adoption rate of modelOps offerings among different end users in key countries, with their regions contributing the most to the market share. For cross-validation, the adoption of the modelOps platform and services among industries, along with different use cases concerning their regions, was identified and extrapolated. Use cases identified in the different areas were given weightage for the market size calculation.

Based on the market numbers, the regional split was determined by primary and secondary sources. The procedure included the analysis of the ModelOps Market’s regional penetration. Based on secondary research, the regional spending on Information and Communications Technology (ICT), socio-economic analysis of each country, strategic vendor analysis of major modelOps platforms providers, and organic and inorganic business development activities of regional and global players were estimated. With the data triangulation procedure and data validation through primaries, the exact values of the overall ModelOps Market size and segments’ size were determined and confirmed using the study.

Top-down and Bottom-up approaches

ModelOps Market Market Top Down and Bottom Up Approach

To know about the assumptions considered for the study, Request for Free Sample Report

Data Triangulation

The market was split into several segments and subsegments after arriving at the overall market size using the market size estimation processes as explained above. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

According to SAS Institute, ModelOps refers to the systematic process through which analytical models are transferred from the data science team to the IT production team, ensuring a consistent cycle of deployment and updates. It is a crucial element in effectively leveraging AI models, yet only a few companies are currently utilizing this approach.

ModelOps (Model Operations) refers to the practices and tools used to streamline production deployment, monitoring, management, and governance of machine learning models. It focuses on ensuring models are reliable, scalable, and maintainable, bridging the gap between data science and IT operations to facilitate continuous delivery and integration of models.

Stakeholders

  • ModelOps Market Platform Providers
  • ModelOps Market Service Providers
  • End-user Industries
  • System Integrators (SIs)
  • Business Intelligence Solution Providers
  • Technology Providers
  • Value-added Resellers (VARs)
  • Government And Regulatory Bodies

Report Objectives

  • To define, describe, and predict the ModelOps Market by offering, model type, application, vertical, and region
  • To describe and forecast the ModelOps Market, in terms of value, by region—North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
  • To provide detailed information regarding major factors influencing the market growth (drivers, restraints, opportunities, and challenges)
  • To strategically analyze micro markets with respect to individual growth trends, prospects, and contributions to the overall ModelOps Market
  • To profile key players and comprehensively analyze their market positions in terms of ranking and core competencies, along with detailing the competitive landscape for market leaders
  • To analyze competitive developments such as joint ventures, mergers and acquisitions, product developments, and ongoing research and development (R&D) in the ModelOps Market
  • To provide the illustrative segmentation, analysis, and projection of the main regional markets

Available Customizations

With the given market data, MarketsandMarkets offers customizations per the company’s specific needs. The following customization options are available for the report:

Product Analysis

  • The product matrix provides a detailed comparison of the product portfolio of each company.

Geographic Analysis as per Feasibility

  • Further breakup of the North American ModelOps Market
  • Further breakup of the European ModelOps Market
  • Further breakup of the Asia Pacific ModelOps Market
  • Further breakup of the Middle East & Africa ModelOps Market
  • Further breakup of the Latin America ModelOps Market

Company Information

  • Detailed analysis and profiling of additional market players (up to five)
Custom Market Research Services

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Report Code
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Published ON
Jun, 2024
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