세계의 ModelOps 시장 : 시장 규모, 점유율, 성장 분석 - 제공별, 모델 유형별, 용도별, 산업별, 지역별 - 예측(-2029년)

The global ModelOps Market is valued at USD 5.4 billion in 2024 and is estimated to reach USD 29.5 billion in 2029, registering a CAGR of 40.2% during the forecast period. The ModelOps Market focuses on optimizing the deployment, monitoring, and management of machine learning models in production. It encompasses automating model deployment, continuous monitoring for performance and data drift, ensuring governance and compliance, orchestrating automation for testing and retraining, and fostering collaboration among data scientists and stakeholders. This market is driven by the demand for scalable, reliable, and agile solutions across industries, enhancing operational efficiency and maximizing the value derived from AI initiatives. As AI and ML technologies advance, ModelOps continues to evolve with innovations in containerization, Kubernetes orchestration, and AI-driven automation, reshaping how organizations operationalize and derive insights from their models.

"By offering, the platforms segment is projected to hold the largest market size during the forecast period."

In the rapidly evolving ModelOps market, platforms offering comprehensive solutions have seized the largest market share due to their integrated approach to managing the entire lifecycle of machine learning models. These platforms streamline operations by consolidating development, training, deployment, and monitoring processes into a unified environment, appealing to enterprises seeking efficiency and collaboration enhancements. Their scalability, supported by robust infrastructure and cloud capabilities, meets the increasing demand for deploying models at scale. Automation features throughout the lifecycle accelerate time-to-market and ensure consistency, while built-in governance mechanisms ensure compliance and reliability, crucial for regulated industries.

"By type, graph-based models are registered to grow at the highest CAGR during the forecast period."

The rapid growth of graph-based model management tools within the ModelOps market stems from their adeptness at handling the intricate nature of modern AI systems. These tools manage complex relationships between models, datasets, and configurations, which traditional databases struggle to accommodate. Their scalability and flexibility make them ideal for dynamic AI environments where rapid evolution and large-scale data handling are the norm. Integrating seamlessly with existing AI platforms enhances visibility and control over model lifecycles, ensuring compliance with regulatory standards and internal governance. They support robust decision-making and automation in deployment processes by providing a clear and auditable lineage of models and data usage. As AI applications expand into new fields like edge computing and personalized medicine, graph-based tools offer a unified solution to effectively manage diverse and distributed environments.

"By application, the continuous integration/continuous deployment segment is projected to hold the largest market size during the forecast period."

Continuous Integration and Continuous Delivery (CI/CD) holds a dominant position within the ModelOps market due to several key factors that highlight its critical role in deploying and managing machine learning models. First and foremost, CI/CD pipelines are foundational in enabling automation throughout the model development lifecycle. In the context of ModelOps, which focuses on operationalizing machine learning models at scale, CI/CD pipelines facilitate the seamless integration of new model versions into production environments. This automation streamlines the process of testing, building, packaging, and deploying models, reducing the manual effort and potential for human error, thereby increasing efficiency and reliability. Further, the demand for CI/CD in ModelOps is driven by the need for agility and speed in deploying models into production. Machine learning models often undergo iterative improvements based on real-world data feedback and evolving business requirements. CI/CD pipelines allow teams to continuously integrate these updates into the operational environment, ensuring that the latest versions of models are always available without disrupting existing processes

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the ModelOps market.

• By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
• By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
• By Region: North America - 30%, Europe - 30%, Asia Pacific - 25%, Middle East & Africa - 10%, and Latin America - 5%

Major vendors offering modelOps solution and services across the globe are IBM (US), Google (US), Oracle (US), SAS Institute (US), AWS (US), Teradata (US), Palantir (US), Veritone (US), Altair (US), c3.ai (US), TIBCO (US), Databricks (US), Giggso (US), Verta (US), ModelOp (US), Comet ML (US), Superwise (Israel), Evidently Al (US), Minitab (US), Seldon (UK), Innominds (US), Datatron (US), Domino Data Lab (US), Arthur (US), Weights & Biases (US), Xenonstack (US), Cnvrg.io (Israel), DataKitchen (US), Haisten AI (US), Sparkling Logic (US), LeewayHertz (US).

Research Coverage

The market study covers modelOps across segments. It aims to estimate the market size and the growth potential across different segments, such as offering, model type, application, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report

The report would provide the market leaders/new entrants with information on the closest approximations of the revenue numbers for the overall market for modelOps and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the market's pulse and provides information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

• Analysis of key drivers (Exponential rise of unstructured data, Rise in digitalization trend), restraints (Discrepancy among data sources impedes the advancement of modelOps, Data Security and Privacy Concerns), opportunities (Empowering modelOps through SDN-enabled network integration, Growing integration of advanced analytical functionalities), and challenges (Rise in need for training and upskilling to address the knowledge gap, Issues related to complexity and diversity of data collected)
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new solutions & service launches in the ModelOps Market.
• Market Development: Comprehensive information about lucrative markets - the report analyses the ModelOps Market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in ModelOps Market strategies; the report also helps stakeholders understand the pulse of the ModelOps Market and provides them with information on key market drivers, restraints, challenges, and opportunities.
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as IBM (US), Oracle (US), SAS Institute(US), Google (US), and AWS (US) among others, in the ModelOps Market.

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
  • 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

• 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

• 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

• 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

• 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

• 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

• 12.1 INTRODUCTION
• 12.2 KEY PLAYERS 228\ (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

• 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

• 14.1 DISCUSSION GUIDE
• 14.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 14.3 CUSTOMIZATION OPTIONS
• 14.4 RELATED REPORTS
• 14.5 AUTHOR DETAILS