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
The global ModelOps Market is valued at USD 5.4 billion in 2024 and is estimated to reach USD 29.5 billion in 2029, registering a CAGR of 40.2% during the forecast period. The ModelOps Market focuses on optimizing the deployment, monitoring, and management of machine learning models in production. It encompasses automating model deployment, continuous monitoring for performance and data drift, ensuring governance and compliance, orchestrating automation for testing and retraining, and fostering collaboration among data scientists and stakeholders. This market is driven by the demand for scalable, reliable, and agile solutions across industries, enhancing operational efficiency and maximizing the value derived from AI initiatives. As AI and ML technologies advance, ModelOps continues to evolve with innovations in containerization, Kubernetes orchestration, and AI-driven automation, reshaping how organizations operationalize and derive insights from their models.
Scope of the Report
Years Considered for the Study
2019-2029
Base Year
2023
Forecast Period
2024-2029
Units Considered
USD (Billion)
Segments
Offering, Model Type, Application, Vertical, and Region
Regions covered
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
"By offering, the platforms segment is projected to hold the largest market size during the forecast period."
In the rapidly evolving ModelOps market, platforms offering comprehensive solutions have seized the largest market share due to their integrated approach to managing the entire lifecycle of machine learning models. These platforms streamline operations by consolidating development, training, deployment, and monitoring processes into a unified environment, appealing to enterprises seeking efficiency and collaboration enhancements. Their scalability, supported by robust infrastructure and cloud capabilities, meets the increasing demand for deploying models at scale. Automation features throughout the lifecycle accelerate time-to-market and ensure consistency, while built-in governance mechanisms ensure compliance and reliability, crucial for regulated industries.
"By type, graph-based models are registered to grow at the highest CAGR during the forecast period."
The rapid growth of graph-based model management tools within the ModelOps market stems from their adeptness at handling the intricate nature of modern AI systems. These tools manage complex relationships between models, datasets, and configurations, which traditional databases struggle to accommodate. Their scalability and flexibility make them ideal for dynamic AI environments where rapid evolution and large-scale data handling are the norm. Integrating seamlessly with existing AI platforms enhances visibility and control over model lifecycles, ensuring compliance with regulatory standards and internal governance. They support robust decision-making and automation in deployment processes by providing a clear and auditable lineage of models and data usage. As AI applications expand into new fields like edge computing and personalized medicine, graph-based tools offer a unified solution to effectively manage diverse and distributed environments.
"By application, the continuous integration/continuous deployment segment is projected to hold the largest market size during the forecast period."
Continuous Integration and Continuous Delivery (CI/CD) holds a dominant position within the ModelOps market due to several key factors that highlight its critical role in deploying and managing machine learning models. First and foremost, CI/CD pipelines are foundational in enabling automation throughout the model development lifecycle. In the context of ModelOps, which focuses on operationalizing machine learning models at scale, CI/CD pipelines facilitate the seamless integration of new model versions into production environments. This automation streamlines the process of testing, building, packaging, and deploying models, reducing the manual effort and potential for human error, thereby increasing efficiency and reliability. Further, the demand for CI/CD in ModelOps is driven by the need for agility and speed in deploying models into production. Machine learning models often undergo iterative improvements based on real-world data feedback and evolving business requirements. CI/CD pipelines allow teams to continuously integrate these updates into the operational environment, ensuring that the latest versions of models are always available without disrupting existing processes
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the ModelOps market.
By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
By Region: North America - 30%, Europe - 30%, Asia Pacific - 25%, Middle East & Africa - 10%, and Latin America - 5%
Major vendors offering modelOps solution and services across the globe are IBM (US), Google (US), Oracle (US), SAS Institute (US), AWS (US), Teradata (US), Palantir (US), Veritone (US), Altair (US), c3.ai (US), TIBCO (US), Databricks (US), Giggso (US), Verta (US), ModelOp (US), Comet ML (US), Superwise (Israel), Evidently Al (US), Minitab (US), Seldon (UK), Innominds (US), Datatron (US), Domino Data Lab (US), Arthur (US), Weights & Biases (US), Xenonstack (US), Cnvrg.io (Israel), DataKitchen (US), Haisten AI (US), Sparkling Logic (US), LeewayHertz (US).
Research Coverage
The market study covers modelOps across segments. It aims to estimate the market size and the growth potential across different segments, such as offering, model type, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall market for modelOps and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (Exponential rise of unstructured data, Rise in digitalization trend), restraints (Discrepancy among data sources impedes the advancement of modelOps, Data Security and Privacy Concerns), opportunities (Empowering modelOps through SDN-enabled network integration, Growing integration of advanced analytical functionalities), and challenges (Rise in need for training and upskilling to address the knowledge gap, Issues related to complexity and diversity of data collected)
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new solutions & service launches in the ModelOps Market.
Market Development: Comprehensive information about lucrative markets - the report analyses the ModelOps Market across varied regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in ModelOps Market strategies; the report also helps stakeholders understand the pulse of the ModelOps Market and provides them with information on key market drivers, restraints, challenges, and opportunities.
Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as IBM (US), Oracle (US), SAS Institute(US), Google (US), and AWS (US) among others, in the ModelOps Market.
TABLE OF CONTENTS
1 INTRODUCTION
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
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
4 PREMIUM INSIGHTS
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
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
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