ModelOps Market

Report Code TC 9051
Published in Jun, 2024, By MarketsandMarkets™
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ModelOps Market by Platforms (AutoML, Monitoring & Observability), Model type (Machine Learning, Graph-based), Application (Dashboard & Reporting, CI/CD, Governance, Risk & Compliance, Batch Scoring, Monitoring & Alerting) - Global Forecast to 2029

Overview

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.

Attractive Opportunities in the ModelOps Market

Asia Pacific

Emerging economies in the Asia Pacific region to provide numerous opportunities across industry verticals.

Continuous integration and deployment (CI/CD) practices boosts the growth of the market.

North America is expected to hold the largest market share during the forecast period. The region is a leader in technological advancements and is home to various cloud analytics vendors.

Acquisitions and mergers will offer lucrative opportunities to the market players in the next five years.

The burgeoning advancements in cloud computing present a lucrative opportunity for the modelOps market, as businesses seek to ensure efficient, scalable, and reliable deployment and monitoring of machine learning models in production environments.

Global ModelOps 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.

Global ModelOps Market Ecosystem Analysis

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.

 

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 Region Holds the Largest Share of the ModelOps Market.

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.

 
 

Recent Developments of ModelOps Market industry

  • 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.

Key Market Players

KEY PLAYERS IN THE ModelOps Market 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)

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Scope of the Report

Report Attribute 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).

 

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Table of Contents

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TITLE
PAGE NO
INTRODUCTION
28
RESEARCH METHODOLOGY
33
EXECUTIVE SUMMARY
45
PREMIUM INSIGHTS
51
MARKET OVERVIEW AND INDUSTRY TRENDS
54
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    - Integration of ModelOps with DevOps and DataOps
    - Rising demand for Explainable AI (XAI)
    - Increasing need to address model drift with ModelOps solutions
    - Rising demand for automated monitoring and alerting capabilities
    RESTRAINTS
    - Shortage of skilled professionals
    - Model interpretability and explainability
    OPPORTUNITIES
    - Integration of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines
    - Enhancements in model versioning and lifecycle management
    CHALLENGES
    - Difficulty in managing intricate dependencies
    - Complexities of integrating with existing systems
    - Disconnect between insights and action
  • 5.3 CASE STUDY ANALYSIS
    CASE STUDY 1: SCRIBD ACCELERATES MODEL DELIVERY USING VERTA’S MACHINE LEARNING OPERATIONS PLATFORM
    CASE STUDY 2: EXSCIENTIA SHORTENS MODEL MONITORING AND PREPARATION FROM DAYS TO HOURS
    CASE STUDY 3: RBC CAPITAL MARKETS ENHANCES BOND TRADING EFFICIENCY USING AI AND MODELOPS CENTER
    CASE STUDY 4: M-KOPA REVOLUTIONIZES MODEL MANAGEMENT PROCESS WITH ASSISTANCE OF W&B
    CASE STUDY 5: CLEARSCAPE ANALYTICS EXPEDITES DEVELOPMENT OF CREDIT RISK PORTFOLIO MODELS FOR SICREDI
    CASE STUDY 6: ENHANCING ML EXPERIMENT MANAGEMENT AT UBER WITH COMET
    CASE STUDY 7: ACCELERATED AI INTEGRATION FOR ENHANCED EVENT RECOMMENDATIONS BY CNVRG.IO
  • 5.4 EVOLUTION OF MODELOPS MARKET
  • 5.5 ECOSYSTEM ANALYSIS
    PLATFORM PROVIDERS
    SERVICE PROVIDERS
    END USERS
    REGULATORY BODIES
  • 5.6 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - Artificial intelligence
    - Cloud computing
    - Knowledge graphs
    - No code
    ADJACENT TECHNOLOGIES
    - Big data & analytics
    - Edge computing
  • 5.7 SUPPLY CHAIN ANALYSIS
  • 5.8 REGULATORY LANDSCAPE
    REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
    REGULATIONS: MODELOPS
    - North America
    - Europe
    - Asia Pacific
    - Middle East & Africa
    - Latin America
  • 5.9 PATENT ANALYSIS
    METHODOLOGY
    PATENTS FILED, BY DOCUMENT TYPE
    INNOVATIONS AND PATENT APPLICATIONS
    - Patent applicants
  • 5.10 KEY CONFERENCES AND EVENTS, 2024-2025
  • 5.11 PORTER’S FIVE FORCES ANALYSIS
    THREAT FROM NEW ENTRANTS
    THREAT OF SUBSTITUTES
    BARGAINING POWER OF SUPPLIERS
    BARGAINING POWER OF BUYERS
    INTENSITY OF COMPETITIVE RIVALRY
  • 5.12 PRICING ANALYSIS
    AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
    INDICATIVE PRICING ANALYSIS, BY OFFERING
  • 5.13 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
  • 5.14 KEY STAKEHOLDERS AND BUYING CRITERIA
    KEY STAKEHOLDERS IN BUYING PROCESS
    BUYING CRITERIA
  • 5.15 INVESTMENT AND FUNDING SCENARIO
  • 5.16 MODELOPS VS. MLOPS
  • 5.17 MODELOPS BEST PRACTICES
MODELOPS MARKET, BY OFFERING
92
  • 6.1 INTRODUCTION
    OFFERING: MARKET DRIVERS
  • 6.2 PLATFORMS
    OPTIMIZING MACHINE LEARNING MODEL LIFECYCLE MANAGEMENT WITH MODELOPS PLATFORMS
    TYPE
    - Development & experimentation platforms
    - Monitoring & observability tools
    - Automated machine learning (AutoML) platforms
    - Performance tracking & management platforms
    - Model explainability & interpretability tools
    - Serving & deployment tools
    - Others
    DEPLOYMENT MODE
    - Cloud
    - On-premises
  • 6.3 SERVICES
    ELEVATING DATA INSIGHTS WITH MODELOPS SERVICES
    CONSULTING
    DEPLOYMENT & INTEGRATION
    SUPPORT & MAINTENANCE
MODELOPS MARKET, BY MODEL TYPE
112
  • 7.1 INTRODUCTION
    MODEL TYPE: MARKET DRIVERS
  • 7.2 ML MODELS
    SEGMENTING, FORECASTING, AND OPTIMIZING MODELOPS FOR COMPETITIVE ADVANTAGE
  • 7.3 GRAPH-BASED MODELS
    GRAPH-BASED MODELS ENHANCE PREDICTIONS AND DECISION-MAKING IN MODELOPS
  • 7.4 RULE & HEURISTIC MODELS
    OPTIMIZING MODELOPS WITH RULE-BASED, HEURISTIC, AND HYBRID MODELS
  • 7.5 LINGUISTIC MODELS
    OPTIMIZING LINGUISTIC MODELS FOR EFFICIENT NLP DEPLOYMENT AND GOVERNANCE
  • 7.6 AGENT-BASED MODELS
    ENHANCING STRATEGIC DECISION-MAKING THROUGH ADVANCED AGENT-BASED MODEL SIMULATION
  • 7.7 BRING YOUR OWN MODELS
    MAXIMIZING OPERATIONAL EFFICIENCY THROUGH SEAMLESS INTEGRATION OF DIVERSE AI MODELS
  • 7.8 OTHER MODEL TYPES
MODELOPS MARKET, BY APPLICATION
122
  • 8.1 INTRODUCTION
    APPLICATION: MARKET DRIVERS
  • 8.2 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT
    IMPLEMENTATION OF CI/CD FOR ACCELERATED DEPLOYMENT OF MACHINE LEARNING MODELS IN MODELOPS
  • 8.3 MONITORING & ALERTING
    ENHANCING MODELOPS WITH RELIABLE MONITORING & ALERTING SERVICES
  • 8.4 DASHBOARD & REPORTING
    DASHBOARD AND REPORTING ENHANCE OPERATIONAL PROCESSES SURROUNDING MACHINE LEARNING MODELS
  • 8.5 MODEL LIFECYCLE MANAGEMENT
    MAXIMIZING AI VALUE THROUGH EFFECTIVE MODEL LIFECYCLE MANAGEMENT
  • 8.6 GOVERNANCE, RISK, & COMPLIANCE
    IMPLEMENTATION OF ROBUST GOVERNANCE, RISK, AND COMPLIANCE (GRC) FRAMEWORK IN MODELOPS FOR EFFECTIVE AI MODEL MANAGEMENT
  • 8.7 PARALLELIZATION & DISTRIBUTED COMPUTING
    EMPOWERING AI/ML SCALABILITY WITH PARALLELIZATION AND DISTRIBUTED COMPUTING IN MODELOPS
  • 8.8 BATCH SCORING
    ENHANCING DATA-DRIVEN DECISION-MAKING WITH BATCH SCORING IN MODELOPS
  • 8.9 OTHER APPLICATIONS
MODELOPS MARKET, BY VERTICAL
133
  • 9.1 INTRODUCTION
    VERTICAL: MARKET DRIVERS
  • 9.2 BFSI
    OPTIMIZING MODELOPS FOR BFSI SECTOR ADVANCEMENTS
  • 9.3 TELECOMMUNICATIONS
    IMPLEMENTING MODELOPS FOR ENHANCED TELECOMMUNICATION EFFICIENCY
  • 9.4 RETAIL & ECOMMERCE
    STREAMLINING AI AND ML DEPLOYMENT TO REVOLUTIONIZE RETAIL AND ECOMMERCE OPERATIONS FOR ENHANCED EFFICIENCY AND CUSTOMER EXPERIENCE
  • 9.5 HEALTHCARE & LIFE SCIENCES
    ENHANCING PATIENT OUTCOMES AND MEDICAL INNOVATION THROUGH MODELOPS IN HEALTHCARE AND LIFE SCIENCES
  • 9.6 GOVERNMENT & DEFENSE
    GOVERNMENTS USE MODELOPS TO APPLY REAL-TIME ANALYTICS IN MISSION-CRITICAL SCENARIOS
  • 9.7 IT/ITES
    IMPLEMENTING MODELOPS FOR EFFICIENT AI/ML LIFECYCLE MANAGEMENT IN IT/ITES
  • 9.8 ENERGY & UTILITIES
    IMPLEMENTING MODELOPS FOR ENERGY AND UTILITIES OPTIMIZATION
  • 9.9 MANUFACTURING
    DEPLOYING MODELOPS FOR ENHANCED MANUFACTURING EFFICIENCY
  • 9.10 TRANSPORTATION & LOGISTICS
    ENHANCING EFFICIENCY AND SAFETY THROUGH MODELOPS IN TRANSPORTATION AND LOGISTICS
  • 9.11 OTHER VERTICALS
MODELOPS MARKET, BY REGION
150
  • 10.1 INTRODUCTION
  • 10.2 NORTH AMERICA
    NORTH AMERICA: MARKET DRIVERS
    NORTH AMERICA: RECESSION IMPACT
    US
    - Widespread adoption of AI and ML technologies across industries to drive market
    CANADA
    - Rising demand for AI and ML solutions in various sectors to drive market
  • 10.3 EUROPE
    EUROPE: MARKET DRIVERS
    EUROPE: RECESSION IMPACT
    UK
    - Increasing AI adoption across industries to drive market
    GERMANY
    - Increasing adoption of AI and ML technologies to drive market
    FRANCE
    - Rising focus on operationalizing AI and ML models to drive market
    ITALY
    - Growing integration of AI and ML across diverse sectors to drive market
    SPAIN
    - Increasing reliance on data-driven decision-making across industries to drive market
    REST OF EUROPE
  • 10.4 ASIA PACIFIC
    ASIA PACIFIC: MARKET DRIVERS
    ASIA PACIFIC: RECESSION IMPACT
    CHINA
    - Rising focus on operationalizing AI models and enhancing business outcomes to drive market
    JAPAN
    - Increasing adoption of AI and ML models in various industries to drive market
    INDIA
    - Rising adoption of AI technologies across sectors to drive market
    SOUTH KOREA
    - Increasing adoption of AI across sectors to drive market
    AUSTRALIA & NEW ZEALAND
    - Growing emphasis on integrating AI solutions to enhance operational efficiency to drive market
    REST OF ASIA PACIFIC
  • 10.5 MIDDLE EAST & AFRICA
    MIDDLE EAST & AFRICA: MODELOPS MARKET DRIVERS
    MIDDLE EAST & AFRICA: RECESSION IMPACT
    UAE
    - Government initiatives toward building knowledge-based economy to drive market
    KSA
    - Growing emphasis on digital transformation and AI integration across sectors to drive market
    QATAR
    - Rising adoption of AI and ML technologies across sectors to drive market
    EGYPT
    - Increasing investments by companies to operationalize AI and ML models to drive market
    SOUTH AFRICA
    - Growing adoption of AI and machine learning models in various sectors to drive market
    REST OF MIDDLE EAST & AFRICA
  • 10.6 LATIN AMERICA
    LATIN AMERICA: MARKET DRIVERS
    LATIN AMERICA: RECESSION IMPACT
    BRAZIL
    - Technological advancements and regulatory compliance to drive market
    MEXICO
    - Increasing digital transformation efforts across industries to drive market
    ARGENTINA
    - Increasing adoption of machine learning and AI technologies in various sectors to drive market
    REST OF LATIN AMERICA
COMPETITIVE LANDSCAPE
207
  • 11.1 OVERVIEW
  • 11.2 STRATEGIES ADOPTED BY KEY PLAYERS
  • 11.3 REVENUE ANALYSIS
  • 11.4 MARKET SHARE ANALYSIS
    MARKET RANKING ANALYSIS
  • 11.5 PRODUCT COMPARATIVE ANALYSIS
  • 11.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2023
    - Company footprint
    - Offering footprint
    - Application footprint
    - Regional footprint
    - Vertical footprint
  • 11.7 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2023
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: START-UPS/SMES, 2023
  • 11.8 COMPETITIVE SCENARIOS AND TRENDS
    PRODUCT LAUNCHES & ENHANCEMENTS
    DEALS
  • 11.9 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
COMPANY PROFILES
228
  • 12.1 INTRODUCTION
  • 12.2 KEY PLAYERS
    IBM
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    - MnM view
    GOOGLE
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    - MnM view
    SAS INSTITUTE
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    - MnM view
    AWS
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    - MnM view
    ORACLE
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    - MnM view
    TERADATA
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    VERITONE
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    ALTAIR
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    C3.AI
    - Business overview
    - Products/Solutions/Services offered
    PALANTIR
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    TIBCO SOFTWARE
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    DOMINO DATA LAB
    - Business overview
    - Products/Solutions/Services offered
    - Recent developments
    DATABRICKS
    GIGGSO
    MODELOP
  • 12.3 OTHER PLAYERS
    VERTA
    COMET ML
    SUPERWISE
    EVIDENTLY AI
    MINITAB
    SELDON
    INNOMINDS
    DATATRON
    ARTHUR AI
    WEIGHTS & BIASES
    XENONSTACK
    CNVRG.IO
    DATAKITCHEN
    HAISTEN AI
    SPARKLING LOGIC
    LEEWAYHERTZ
ADJACENT AND RELATED MARKETS
285
  • 13.1 INTRODUCTION
  • 13.2 MLOPS
    MARKET DEFINITION
    MARKET OVERVIEW
    - MLOps market, by component
    - MLOps market, by deployment mode
    - MLOps market, by organization size
    - MLOps market, by vertical
    - MLOps market, by region
  • 13.3 ARTIFICIAL INTELLIGENCE (AI) MARKET
    MARKET DEFINITION
    MARKET OVERVIEW
    - Artificial intelligence (AI) market, by offering
    - Artificial intelligence (AI) market, by hardware
    - Artificial intelligence (AI) market, by software
    - Artificial intelligence (AI) market, by services
    - Artificial intelligence (AI) market, by technology
    - Artificial intelligence (AI) market, by business function
    - Artificial intelligence (AI) market, by vertical
    - Artificial intelligence (AI) market, by region
APPENDIX
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
 
  • 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 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 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 MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 23 MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 24 MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 25 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 MARKET FOR PLATFORMS, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 41 CLOUD: MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 42 CLOUD: MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 43 ON-PREMISES: MARKET FOR PLATFORMS, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 44 ON-PREMISES: MARKET FOR PLATFORMS, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 45 MODELOPS MARKET FOR SERVICES, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 46 MARKET FOR SERVICES, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 47 MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 48 MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 49 CONSULTING: MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 50 CONSULTING: MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 51 DEPLOYMENT & INTEGRATION: MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 52 DEPLOYMENT & INTEGRATION: MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 53 SUPPORT & MAINTENANCE: MARKET FOR SERVICES, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 54 SUPPORT & MAINTENANCE: MARKET FOR SERVICES, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 55 MODELOPS MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 56 MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 57 ML MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 58 ML MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 59 GRAPH-BASED MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 60 GRAPH-BASED MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 61 RULE & HEURISTIC MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 62 RULE & HEURISTIC MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 63 LINGUISTIC MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 64 LINGUISTIC MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 65 AGENT-BASED MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 66 AGENT-BASED MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 67 BRING YOUR OWN MODELS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 68 BRING YOUR OWN MODELS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 69 OTHER MODEL TYPES: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 70 OTHER MODEL TYPES: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 71 MODELOPS MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 72 MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 73 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 74 CONTINUOUS INTEGRATION/CONTINUOUS DEPLOYMENT: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 75 MONITORING & ALERTING: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 76 MONITORING & ALERTING: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 77 DASHBOARD & REPORTING: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 78 DASHBOARD & REPORTING: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 79 MODEL LIFECYCLE MANAGEMENT: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 80 MODEL LIFECYCLE MANAGEMENT: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 81 GOVERNANCE, RISK, & COMPLIANCE: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 82 GOVERNANCE, RISK, & COMPLIANCE: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 83 PARALLELIZATION & DISTRIBUTED COMPUTING: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 84 PARALLELIZATION & DISTRIBUTED COMPUTING: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 85 BATCH SCORING: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 86 BATCH SCORING: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 87 OTHER APPLICATIONS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 88 OTHER APPLICATIONS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 89 MODELOPS MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 90 MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 91 BANKING, FINANCIAL SERVICES, AND INSURANCE: USE CASES
  • TABLE 92 BFSI: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 93 BFSI: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 94 TELECOMMUNICATIONS: USE CASES
  • TABLE 95 TELECOMMUNICATIONS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 96 TELECOMMUNICATIONS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 97 RETAIL & ECOMMERCE: USE CASES
  • TABLE 98 RETAIL & ECOMMERCE: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 99 RETAIL & ECOMMERCE: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 100 HEALTHCARE & LIFE SCIENCES: USE CASES
  • TABLE 101 HEALTHCARE & LIFE SCIENCES: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 102 HEALTHCARE & LIFE SCIENCES: 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: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 106 IT/ITES: USE CASES
  • TABLE 107 IT/ITES: 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: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 111 ENERGY & UTILITIES: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 112 MANUFACTURING: USE CASES
  • TABLE 113 MANUFACTURING: 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: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 117 TRANSPORTATION & LOGISTICS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 118 OTHER VERTICALS: USE CASES
  • TABLE 119 OTHER VERTICALS: MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 120 OTHER VERTICALS: MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 121 MARKET, BY REGION, 2019–2023 (USD MILLION)
  • TABLE 122 MARKET, BY REGION, 2024–2029 (USD MILLION)
  • TABLE 123 NORTH AMERICA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 124 NORTH AMERICA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 125 NORTH AMERICA: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 126 NORTH AMERICA: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 127 NORTH AMERICA: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 128 NORTH AMERICA: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 129 NORTH AMERICA: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 130 NORTH AMERICA: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 131 NORTH AMERICA: MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 132 NORTH AMERICA: MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 133 NORTH AMERICA: MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 134 NORTH AMERICA: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 135 NORTH AMERICA: MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 136 NORTH AMERICA: MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 137 NORTH AMERICA: MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
  • TABLE 138 NORTH AMERICA: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
  • TABLE 139 US: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 140 US: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 141 US: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 142 US: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 143 US: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 144 US: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 145 US: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 146 US: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 147 CANADA: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 148 CANADA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 149 CANADA: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 150 CANADA: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 151 CANADA: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 152 CANADA: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 153 CANADA: MODELOPS MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 154 CANADA: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 155 EUROPE: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 156 EUROPE: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 157 EUROPE: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 158 EUROPE: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 159 EUROPE: MODELOPS MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 160 EUROPE: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 161 EUROPE: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 162 EUROPE: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 163 EUROPE: MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 164 EUROPE: MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 165 EUROPE: MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 166 EUROPE: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 167 EUROPE: MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 168 EUROPE: MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 169 EUROPE: MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
  • TABLE 170 EUROPE: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
  • TABLE 171 UK: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 172 UK: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 173 UK: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 174 UK: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 175 UK: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 176 UK: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 177 UK: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 178 UK: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 179 ASIA PACIFIC: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 180 ASIA PACIFIC: MODELOPS MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 181 ASIA PACIFIC: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 182 ASIA PACIFIC: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 183 ASIA PACIFIC: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 184 ASIA PACIFIC: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 185 ASIA PACIFIC: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 186 ASIA PACIFIC: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 187 ASIA PACIFIC: MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 188 ASIA PACIFIC: MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 189 ASIA PACIFIC: MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 190 ASIA PACIFIC: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 191 ASIA PACIFIC: MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 192 ASIA PACIFIC: MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 193 ASIA PACIFIC: MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
  • TABLE 194 ASIA PACIFIC: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
  • TABLE 195 CHINA: MODELOPS MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 196 CHINA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 197 CHINA: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 198 CHINA: MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 199 CHINA: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 200 CHINA: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 201 CHINA: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 202 CHINA: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 203 MIDDLE EAST & AFRICA: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 204 MIDDLE EAST & AFRICA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 205 MIDDLE EAST & AFRICA: 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: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 208 MIDDLE EAST & AFRICA: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 209 MIDDLE EAST & AFRICA: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 210 MIDDLE EAST & AFRICA: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 211 MIDDLE EAST & AFRICA: MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 212 MIDDLE EAST & AFRICA: MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 213 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 214 MIDDLE EAST & AFRICA: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 215 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 216 MIDDLE EAST & AFRICA: MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 217 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
  • TABLE 218 MIDDLE EAST & AFRICA: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
  • TABLE 219 LATIN AMERICA: MARKET, BY OFFERING, 2019–2023 (USD MILLION)
  • TABLE 220 LATIN AMERICA: MARKET, BY OFFERING, 2024–2029 (USD MILLION)
  • TABLE 221 LATIN AMERICA: MARKET, BY TYPE, 2019–2023 (USD MILLION)
  • TABLE 222 LATIN AMERICA: MODELOPS MARKET, BY TYPE, 2024–2029 (USD MILLION)
  • TABLE 223 LATIN AMERICA: MARKET, BY SERVICE, 2019–2023 (USD MILLION)
  • TABLE 224 LATIN AMERICA: MARKET, BY SERVICE, 2024–2029 (USD MILLION)
  • TABLE 225 LATIN AMERICA: MARKET, BY DEPLOYMENT MODE, 2019–2023 (USD MILLION)
  • TABLE 226 LATIN AMERICA: MARKET, BY DEPLOYMENT MODE, 2024–2029 (USD MILLION)
  • TABLE 227 LATIN AMERICA: MARKET, BY MODEL TYPE, 2019–2023 (USD MILLION)
  • TABLE 228 LATIN AMERICA: MARKET, BY MODEL TYPE, 2024–2029 (USD MILLION)
  • TABLE 229 LATIN AMERICA: MARKET, BY APPLICATION, 2019–2023 (USD MILLION)
  • TABLE 230 LATIN AMERICA: MARKET, BY APPLICATION, 2024–2029 (USD MILLION)
  • TABLE 231 LATIN AMERICA: MARKET, BY VERTICAL, 2019–2023 (USD MILLION)
  • TABLE 232 LATIN AMERICA: MARKET, BY VERTICAL, 2024–2029 (USD MILLION)
  • TABLE 233 LATIN AMERICA: MARKET, BY COUNTRY, 2019–2023 (USD MILLION)
  • TABLE 234 LATIN AMERICA: MARKET, BY COUNTRY, 2024–2029 (USD MILLION)
  • TABLE 235 OVERVIEW OF STRATEGIES ADOPTED BY KEY MODELOPS VENDORS
  • TABLE 236 MARKET: DEGREE OF COMPETITION
  • TABLE 237 MODELOPS MARKET: OFFERING FOOTPRINT (12 COMPANIES)
  • TABLE 238 MARKET: APPLICATION FOOTPRINT (12 COMPANIES)
  • TABLE 239 MARKET: REGIONAL FOOTPRINT (12 COMPANIES)
  • TABLE 240 MARKET: VERTICAL FOOTPRINT (12 COMPANIES)
  • TABLE 241 MARKET: DETAILED LIST OF KEY START-UPS/SMES
  • TABLE 242 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
 
  • FIGURE 1 MODELOPS MARKET: RESEARCH DESIGN
  • FIGURE 2 DATA TRIANGULATION
  • FIGURE 3 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: MARKET SNAPSHOT
  • FIGURE 44 ASIA PACIFIC: 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 MARKET: COMPANY FOOTPRINT (12 COMPANIES)
  • FIGURE 50 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

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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

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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)

Previous Versions of this Report

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