Machine Learning as a Service Market Synopsis

Machine Learning as a Service Market Size Was Valued at USD 35.40 Billion in 2023 and is Projected to Reach USD 578.54 Billion by 2032, Growing at a CAGR of 36.4 % From 2024-2032.

Machine Learning as a Service (MLaaS) is a cloud-based platform that offers access to machine learning tools, algorithms, and infrastructure, enabling users to develop, train, and deploy models without extensive expertise. It offers scalability, flexibility, and accessibility, democratizing AI and making it more accessible to businesses of all sizes and industries, driving innovation, and accelerating the adoption of intelligent technologies.

• Machine Learning as a Service (MLaaS) is a transformative solution that offers numerous advantages and meets growing demand. It provides accessibility to ML tools and expertise without requiring extensive in-house resources, making it feasible for businesses of all sizes to implement ML solutions. MLaaS platforms offer scalability, allowing organizations to handle large datasets and complex models efficiently. This democratization fosters innovation and drives competitiveness across industries.
• MLaaS simplifies the development and deployment of ML models, reducing the time and resources needed to bring AI-driven solutions to market. Market trends show a growing adoption of MLaaS across various sectors, including finance, healthcare, retail, and manufacturing. Companies are leveraging MLaaS to gain insights from data, automate processes, enhance customer experiences, and optimize operations. Emerging trends like AutoML, federated learning, and edge AI are shaping the MLaaS landscape, simplifying the ML model development process, addressing privacy and data security concerns, and enabling real-time inference and decision-making.
• MLaaS helps in compliance with regulatory requirements and data privacy laws by providing secure environments for machine learning model development and deployment. This is crucial in industries like finance and healthcare, where sensitive data handling is strictly regulated. MLaaS also encourages collaboration among data scientists and developers through shared platforms and libraries, accelerating innovation. As businesses seek actionable insights from their data, MLaaS becomes a strategic enabler, unlocking machine learning's full potential.

Machine Learning as a Service Market Trend Analysis

Accessibility and Democratization of AI

• The accessibility and democratization of AI stand as pivotal drivers propelling the adoption of Machine Learning as a Service (MLaaS). These platforms democratize AI access by offering user-friendly tools, resources, and infrastructure, irrespective of an organization's scale or technical proficiency. Such accessibility empowers businesses of varying sizes to harness machine learning's potential without the necessity for extensive in-house resources, specialized skills, or significant initial investments.
• MLaaS solutions furnish pre-built models, algorithms, and APIs that abstract away the complexities of machine learning, making it reachable to users across different proficiency levels. This democratization broadens the spectrum of users, including data scientists, developers, and business analysts, enabling them to integrate AI capabilities seamlessly into their applications and workflows. Consequently, this fosters innovation and competitiveness across industries. Ultimately, through democratizing AI access, MLaaS cultivates a more inclusive and diverse user ecosystem while expediting AI-driven advancements in businesses globally.

Improved connectivity and increase in data from IoT platforms

• The surge in connectivity and data generated by IoT platforms presents a notable opportunity for Machine Learning as a Service (MLaaS) providers. MLaaS platforms can capitalize on this abundance of data to offer sophisticated analytics and insights. By harnessing machine learning algorithms, businesses can extract valuable insights from IoT data streams, leading to enhanced decision-making, predictive maintenance, and operational efficiency.
• MLaaS solutions enable real-time optimization and proactive issue detection across IoT deployments. Additionally, MLaaS facilitates the development of tailored solutions for specific IoT applications, such as smart manufacturing, healthcare monitoring, and predictive maintenance. In summary, the increased connectivity and data flow from IoT platforms offers a promising avenue for MLaaS providers to deliver innovative solutions, driving business growth, efficiency, and competitiveness in today's dynamic digital landscape.
• MLaaS can improve IoT security by utilizing machine learning algorithms to detect and mitigate cybersecurity threats in real time. This integration with IoT platforms allows edge computing, reducing latency and bandwidth usage, and facilitating real-time decision-making in applications like autonomous vehicles and remote monitoring. This synergy opens up opportunities for businesses to harness data-driven insights and drive transformative changes across industries.

Machine Learning as a Service Market Segment Analysis:

Machine Learning as a Service Market Segmented on the basis of Type, Deployment Model, Organization Size, Application, and End User.

By Type, Model Training and Deployment segment is expected to dominate the market during the forecast period

• The Model Training and Deployment segment is expected to dominate the Machine Learning as a Service (MLaaS) market due to its fundamental role in the machine learning workflow. Organizations prioritize investments in MLaaS solutions that offer robust capabilities for model development, training, and deployment. These services cater to various use cases and industries, including predictive analytics, natural language processing, computer vision, and recommendation systems.
• The increasing complexity of machine learning models and datasets demands sophisticated tools and infrastructure for efficient training and deployment. MLaaS providers with scalable computing resources, advanced algorithms, and model optimization techniques gain a competitive edge. The growing demand for AI-driven insights and automation fuels the adoption of model training and deployment services.
• Advancements in technologies like AutoML and federated learning automate and streamline the model development process, making it more accessible to users with varying levels of expertise.

By Deployment Model, Public Cloud segment is expected to dominate the market during the forecast period

• The Public Cloud segment is expected to dominate the Machine Learning as a Service (MLaaS) market due to its extensive infrastructure, resources, and services. Providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) invest heavily in cutting-edge machine learning technologies, frameworks, and tools, providing scalable and cost-effective solutions. Public cloud offers unmatched scalability, allowing organizations to scale ML workloads based on demand without physical infrastructure constraints. It also offers a diverse ecosystem of services and integrations, enabling seamless development, deployment, and management of machine learning models.
• Public cloud providers prioritize security and compliance, offering robust data encryption, access controls, and compliance certifications to safeguard sensitive data and ensure regulatory compliance. This focus on security ensures the safety and integrity of data and models in the cloud environment.
• Public cloud providers continue to innovate rapidly, introducing new features, services, and partnerships to meet evolving customer needs and industry trends. This innovation cycle drives continuous improvements in the performance, reliability, and usability of MLaaS offerings, solidifying their dominance in the MLaaS market.

Machine Learning as a Service Market Regional Insights:

Asia Pacific is Expected to Dominate the Market Over the Forecast period

• The Asia Pacific (APAC) region is poised to dominate the Machine Learning as a Service (MLaaS) market due to its fast-growing economies, high internet and mobile penetration, and vibrant start-up ecosystem. These countries are undergoing rapid digital transformation, leading to a demand for MLaaS solutions to harness data and AI for innovation, efficiency, and competitiveness. The region's large and increasingly connected population creates vast opportunities for MLaaS providers to offer AI-driven solutions for personalized services, e-commerce, and digital media.
• Government initiatives and support in APAC nations are actively promoting digital technologies and innovation, encouraging the adoption of MLaaS among businesses, particularly in sectors like healthcare, finance, and manufacturing. The increasing adoption of cloud services in APAC further accelerates the growth of MLaaS, with leading providers expanding their presence to meet the evolving needs of businesses across industries.
• The Asia Pacific region offers diverse and dynamic markets for MLaaS providers, allowing them to tailor their offerings to local businesses' needs. The region's cultural richness encourages innovation and collaboration, leading to the development of cutting-edge solutions. Emerging technologies like IoT, 5G, and edge computing are expected to accelerate demand for MLaaS, making it a powerhouse in the global market.

Machine Learning as a Service Market Top Key Players:

• Amazon Web Services (AWS) (US)
• Google Cloud (US)
• Microsoft Azure (US)
• IBM Watson Studio (US)
• Oracle Machine Learning (US)
• SAS Viya (US)
• Databricks (US)
• DataRobot (US)
• H2O.ai (US)
• Cloudera (US)
• RapidMiner (US)
• Domino Data Lab (US)
• BigML (US)
• Algorithmia (US)
• TensorFlow Extended (TFX) (US)
• Explorium (US)
• C3.ai (US)
• Auger.AI (US)
• Sagemaker Autopilot (US)
• Seldon Core (UK)
• Dataiku (France)
• Alibaba Cloud (China), and Other Major Players.

Key Industry Developments in the Machine Learning as a Service Market:

• In December 2023, Union Bank of India, a prominent public sector bank in India, partnered with Accenture to develop a scalable and secure enterprise data lake platform equipped with advanced analytics and reporting features. This initiative aims to improve the bank's operational efficiency and strengthen its capacity to deliver customer-centric banking services while managing risks effectively. Leveraging machine learning, predictive analytics, and artificial intelligence, the platform will analyze both structured and unstructured data from internal and external sources to generate actionable insights.
• In June 2023, Zain Tech, the digital solutions arm of Zain Group, entered into a memorandum of understanding (MoU) with Mastercard to collaborate on innovative, data-driven solutions for organizations across the Middle East and North Africa (MENA) region. This partnership is designed to streamline clients' operations, leading to increased productivity and cost savings.
• In February 2024, Wipro Limited a prominent technology services and consulting company, expanded its partnership with IBM to offer new AI services and support to clients. They announced the launch of the Wipro Enterprise Artificial Intelligence (AI)-Ready Platform, enabling clients to establish their enterprise-level, fully integrated, and customized AI environments. Leveraging the IBM Watsonx AI and data platform, including Watsonx.ai, Watsonx. data, and Watson. governance, along with AI assistants, the platform provided clients with an interoperable service, accelerating AI adoption.

1. INTRODUCTION
   1. RESEARCH OBJECTIVES
   2. RESEARCH METHODOLOGY
   3. RESEARCH PROCESS
   4. SCOPE AND COVERAGE
      1. Market Definition
      2. Key Questions Answered
   5. MARKET SEGMENTATION
2. EXECUTIVE SUMMARY
3. MARKET OVERVIEW
4. GROWTH OPPORTUNITIES BY SEGMENT
5. MARKET LANDSCAPE
   1. PORTER’S FIVE FORCES ANALYSIS
      1. Bargaining Power Of Supplier
      2. Threat Of New Entrants
      3. Threat Of Substitutes
      4. Competitive Rivalry
      5. Bargaining Power Among Buyers
   2. INDUSTRY VALUE CHAIN ANALYSIS
   3. MARKET DYNAMICS
      1. Drivers
      2. Restraints
      3. Opportunities
      4. Challenges
   4. MARKET TREND ANALYSIS
   5. REGULATORY LANDSCAPE
   6. PESTLE ANALYSIS
   7. PRICE TREND ANALYSIS
   8. PATENT ANALYSIS
   9. TECHNOLOGY EVALUATION
   10. MARKET IMPACT OF THE RUSSIA-UKRAINE WAR
       1. Geopolitical Market Disruptions
       2. Supply Chain Disruptions
       3. Instability in Emerging Markets
   11. ECOSYSTEM
6. MACHINE LEARNING AS A SERVICE MARKET BY TYPE (2017-2032)
   1. MACHINE LEARNING AS A SERVICE MARKET SNAPSHOT AND GROWTH ENGINE
   2. MARKET OVERVIEW
   3. MODEL TRAINING AND DEPLOYMENT
      1. Introduction And Market Overview
      2. Historic And Forecasted Market Size in Value (2017 – 2032F)
      3. Historic And Forecasted Market Size in Volume (2017 – 2032F)
      4. Key Market Trends, Growth Factors And Opportunities
      5. Geographic Segmentation Analysis
   4. PRE-TRAINED MODELS
   5. MACHINE LEARNING APIS
   6. AUTOML SERVICES
7. MACHINE LEARNING AS A SERVICE MARKET BY DEPLOYMENT MODEL (2017-2032)
   1. MACHINE LEARNING AS A SERVICE MARKET SNAPSHOT AND GROWTH ENGINE
   2. MARKET OVERVIEW
   3. PUBLIC CLOUD
      1. Introduction And Market Overview
      2. Historic And Forecasted Market Size in Value (2017 – 2032F)
      3. Historic And Forecasted Market Size in Volume (2017 – 2032F)
      4. Key Market Trends, Growth Factors And Opportunities
      5. Geographic Segmentation Analysis
   4. PRIVATE CLOUD
   5. HYBRID CLOUD
8. MACHINE LEARNING AS A SERVICE MARKET BY ORGANIZATION SIZE (2017-2032)
   1. MACHINE LEARNING AS A SERVICE MARKET SNAPSHOT AND GROWTH ENGINE
   2. MARKET OVERVIEW
   3. SMALL AND MEDIUM ENTERPRISES
      1. Introduction And Market Overview
      2. Historic And Forecasted Market Size in Value (2017 – 2032F)
      3. Historic And Forecasted Market Size in Volume (2017 – 2032F)
      4. Key Market Trends, Growth Factors And Opportunities
      5. Geographic Segmentation Analysis
   4. LARGE ENTERPRISES
9. MACHINE LEARNING AS A SERVICE MARKET BY APPLICATION (2017-2032)
   1. MACHINE LEARNING AS A SERVICE MARKET SNAPSHOT AND GROWTH ENGINE
   2. MARKET OVERVIEW
   3. MARKETING AND ADVERTISEMENT
      1. Introduction And Market Overview
      2. Historic And Forecasted Market Size in Value (2017 – 2032F)
      3. Historic And Forecasted Market Size in Volume (2017 – 2032F)
      4. Key Market Trends, Growth Factors And Opportunities
      5. Geographic Segmentation Analysis
   4. PREDICTIVE MAINTENANCE
   5. AUTOMATED NETWORK MANAGEMENT
   6. FRAUD DETECTION
   7. RISK ANALYTICS
10. MACHINE LEARNING AS A SERVICE MARKET BY END USER (2017-2032)
    1. MACHINE LEARNING AS A SERVICE MARKET SNAPSHOT AND GROWTH ENGINE
    2. MARKET OVERVIEW
    3. IT AND TELECOM
       1. Introduction And Market Overview
       2. Historic And Forecasted Market Size in Value (2017 – 2032F)
       3. Historic And Forecasted Market Size in Volume (2017 – 2032F)
       4. Key Market Trends, Growth Factors And Opportunities
       5. Geographic Segmentation Analysis
    4. AUTOMOTIVE
    5. HEALTHCARE
    6. AEROSPACE AND DEFENSE
    7. RETAIL
    8. GOVERNMENT
11. COMPANY PROFILES AND COMPETITIVE ANALYSIS
    1. COMPETITIVE LANDSCAPE
       1. Competitive Positioning
       2. Machine Learning as a Service Market Share By Manufacturer (2023)
       3. Industry BCG Matrix
       4. Heat Map Analysis
       5. Mergers & Acquisitions
    2. AMAZON WEB SERVICES (AWS) (US)
       1. Company Overview
       2. Key Executives
       3. Company Snapshot
       4. Role of the Company in the Market
       5. Sustainability and Social Responsibility
       6. Operating Business Segments
       7. Product Portfolio
       8. Business Performance (Production Volume, Sales Volume, Sales Margin, Production Capacity, Capacity Utilization Rate)
       9. Key Strategic Moves And Recent Developments
       10. SWOT Analysis
    3. GOOGLE CLOUD (US)
    4. MICROSOFT AZURE (US)
    5. IBM WATSON STUDIO (US)
    6. ORACLE MACHINE LEARNING (US)
    7. SAS VIYA (US)
    8. DATABRICKS (US)
    9. DATAROBOT (US)
    10. H2O.AI (US)
    11. CLOUDERA (US)
    12. RAPIDMINER (US)
    13. DOMINO DATA LAB (US)
    14. BIGML (US)
    15. ALGORITHMIA (US)
    16. TENSORFLOW EXTENDED (TFX) (US)
    17. EXPLORIUM (US)
    18. C3.AI (US)
    19. AUGER.AI (US)
    20. SAGEMAKER AUTOPILOT (US)
    21. SELDON CORE (UK)
    22. DATAIKU (FRANCE)
    23. ALIBABA CLOUD (CHINA)
12. GLOBAL MACHINE LEARNING AS A SERVICE MARKET BY REGION
    1. OVERVIEW
    2. NORTH AMERICA
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Type
       4. Historic And Forecasted Market Size By Deployment Model
       5. Historic And Forecasted Market Size By Organization Size
       6. Historic And Forecasted Market Size By Application
       7. Historic And Forecasted Market Size By End User
       8. Historic And Forecasted Market Size By Country
          1. USA
          2. Canada
          3. Mexico
    3. EASTERN EUROPE
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Segments
       4. Historic And Forecasted Market Size By Country
          1. Russia
          2. Bulgaria
          3. The Czech Republic
          4. Hungary
          5. Poland
          6. Romania
          7. Rest Of Eastern Europe
    4. WESTERN EUROPE
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Segments
       4. Historic And Forecasted Market Size By Country
          1. Germany
          2. United Kingdom
          3. France
          4. The Netherlands
          5. Italy
          6. Spain
          7. Rest Of Western Europe
    5. ASIA PACIFIC
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Segments
       4. Historic And Forecasted Market Size By Country
          1. China
          2. India
          3. Japan
          4. South Korea
          5. Malaysia
          6. Thailand
          7. Vietnam
          8. The Philippines
          9. Australia
          10. New-Zealand
          11. Rest Of APAC
    6. MIDDLE EAST & AFRICA
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Segments
       4. Historic And Forecasted Market Size By Country
          1. Turkey
          2. Bahrain
          3. Kuwait
          4. Saudi Arabia
          5. Qatar
          6. UAE
          7. Israel
          8. South Africa
    7. SOUTH AMERICA
       1. Key Market Trends, Growth Factors And Opportunities
       2. Key Manufacturers
       3. Historic And Forecasted Market Size By Segments
       4. Historic And Forecasted Market Size By Country
          1. Brazil
          2. Argentina
          3. Rest of South America
13. INVESTMENT ANALYSIS
14. ANALYST VIEWPOINT AND CONCLUSION
    1. Recommendations and Concluding Analysis
    2. Potential Market Strategies

LIST OF TABLES

TABLE 001. EXECUTIVE SUMMARY
TABLE 002. MACHINE LEARNING AS A SERVICE MARKET BARGAINING POWER OF SUPPLIERS
TABLE 003. MACHINE LEARNING AS A SERVICE MARKET BARGAINING POWER OF CUSTOMERS
TABLE 004. MACHINE LEARNING AS A SERVICE MARKET COMPETITIVE RIVALRY
TABLE 005. MACHINE LEARNING AS A SERVICE MARKET THREAT OF NEW ENTRANTS
TABLE 006. MACHINE LEARNING AS A SERVICE MARKET THREAT OF SUBSTITUTES
TABLE 007. MACHINE LEARNING AS A SERVICE MARKET BY COMPONENT
TABLE 008. SOLUTIONS MARKET OVERVIEW (2016-2028)
TABLE 009. SERVICES MARKET OVERVIEW (2016-2028)
TABLE 010. MACHINE LEARNING AS A SERVICE MARKET BY APPLICATION
TABLE 011. NATURAL LANGUAGE PROCESSING MARKET OVERVIEW (2016-2028)
TABLE 012. MARKETING & ADVERTISING MARKET OVERVIEW (2016-2028)
TABLE 013. FRAUD DETECTION & RISK MANAGEMENT MARKET OVERVIEW (2016-2028)
TABLE 014. PREDICTIVE ANALYTICS MARKET OVERVIEW (2016-2028)
TABLE 015. OTHERS MARKET OVERVIEW (2016-2028)
TABLE 016. MACHINE LEARNING AS A SERVICE MARKET BY END-USE INDUSTRY
TABLE 017. BFSI MARKET OVERVIEW (2016-2028)
TABLE 018. GOVERNMENT & DEFENSE MARKET OVERVIEW (2016-2028)
TABLE 019. HEALTHCARE & LIFE SCIENCES MARKET OVERVIEW (2016-2028)
TABLE 020. TELECOMMUNICATIONS MARKET OVERVIEW (2016-2028)
TABLE 021. RETAIL MARKET OVERVIEW (2016-2028)
TABLE 022. OTHERS MARKET OVERVIEW (2016-2028)
TABLE 023. NORTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY COMPONENT (2016-2028)
TABLE 024. NORTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY APPLICATION (2016-2028)
TABLE 025. NORTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY END-USE INDUSTRY (2016-2028)
TABLE 026. N MACHINE LEARNING AS A SERVICE MARKET, BY COUNTRY (2016-2028)
TABLE 027. EUROPE MACHINE LEARNING AS A SERVICE MARKET, BY COMPONENT (2016-2028)
TABLE 028. EUROPE MACHINE LEARNING AS A SERVICE MARKET, BY APPLICATION (2016-2028)
TABLE 029. EUROPE MACHINE LEARNING AS A SERVICE MARKET, BY END-USE INDUSTRY (2016-2028)
TABLE 030. MACHINE LEARNING AS A SERVICE MARKET, BY COUNTRY (2016-2028)
TABLE 031. ASIA PACIFIC MACHINE LEARNING AS A SERVICE MARKET, BY COMPONENT (2016-2028)
TABLE 032. ASIA PACIFIC MACHINE LEARNING AS A SERVICE MARKET, BY APPLICATION (2016-2028)
TABLE 033. ASIA PACIFIC MACHINE LEARNING AS A SERVICE MARKET, BY END-USE INDUSTRY (2016-2028)
TABLE 034. MACHINE LEARNING AS A SERVICE MARKET, BY COUNTRY (2016-2028)
TABLE 035. MIDDLE EAST & AFRICA MACHINE LEARNING AS A SERVICE MARKET, BY COMPONENT (2016-2028)
TABLE 036. MIDDLE EAST & AFRICA MACHINE LEARNING AS A SERVICE MARKET, BY APPLICATION (2016-2028)
TABLE 037. MIDDLE EAST & AFRICA MACHINE LEARNING AS A SERVICE MARKET, BY END-USE INDUSTRY (2016-2028)
TABLE 038. MACHINE LEARNING AS A SERVICE MARKET, BY COUNTRY (2016-2028)
TABLE 039. SOUTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY COMPONENT (2016-2028)
TABLE 040. SOUTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY APPLICATION (2016-2028)
TABLE 041. SOUTH AMERICA MACHINE LEARNING AS A SERVICE MARKET, BY END-USE INDUSTRY (2016-2028)
TABLE 042. MACHINE LEARNING AS A SERVICE MARKET, BY COUNTRY (2016-2028)
TABLE 043. GOOGLE LLC: SNAPSHOT
TABLE 044. GOOGLE LLC: BUSINESS PERFORMANCE
TABLE 045. GOOGLE LLC: PRODUCT PORTFOLIO
TABLE 046. GOOGLE LLC: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 046. AMAZON WEB SERVICES: SNAPSHOT
TABLE 047. AMAZON WEB SERVICES: BUSINESS PERFORMANCE
TABLE 048. AMAZON WEB SERVICES: PRODUCT PORTFOLIO
TABLE 049. AMAZON WEB SERVICES: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 049. BIGML: SNAPSHOT
TABLE 050. BIGML: BUSINESS PERFORMANCE
TABLE 051. BIGML: PRODUCT PORTFOLIO
TABLE 052. BIGML: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 052. IBM CORPORATION: SNAPSHOT
TABLE 053. IBM CORPORATION: BUSINESS PERFORMANCE
TABLE 054. IBM CORPORATION: PRODUCT PORTFOLIO
TABLE 055. IBM CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 055. INTEL CORPORATION: SNAPSHOT
TABLE 056. INTEL CORPORATION: BUSINESS PERFORMANCE
TABLE 057. INTEL CORPORATION: PRODUCT PORTFOLIO
TABLE 058. INTEL CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 058. MICROSOFT CORPORATION: SNAPSHOT
TABLE 059. MICROSOFT CORPORATION: BUSINESS PERFORMANCE
TABLE 060. MICROSOFT CORPORATION: PRODUCT PORTFOLIO
TABLE 061. MICROSOFT CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 061. ORACLE CORPORATION: SNAPSHOT
TABLE 062. ORACLE CORPORATION: BUSINESS PERFORMANCE
TABLE 063. ORACLE CORPORATION: PRODUCT PORTFOLIO
TABLE 064. ORACLE CORPORATION: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 064. HEWLETT PACKARD ENTERPRISES: SNAPSHOT
TABLE 065. HEWLETT PACKARD ENTERPRISES: BUSINESS PERFORMANCE
TABLE 066. HEWLETT PACKARD ENTERPRISES: PRODUCT PORTFOLIO
TABLE 067. HEWLETT PACKARD ENTERPRISES: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 067. AT&T: SNAPSHOT
TABLE 068. AT&T: BUSINESS PERFORMANCE
TABLE 069. AT&T: PRODUCT PORTFOLIO
TABLE 070. AT&T: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 070. FICO: SNAPSHOT
TABLE 071. FICO: BUSINESS PERFORMANCE
TABLE 072. FICO: PRODUCT PORTFOLIO
TABLE 073. FICO: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 073. SIFT-SCIENCE: SNAPSHOT
TABLE 074. SIFT-SCIENCE: BUSINESS PERFORMANCE
TABLE 075. SIFT-SCIENCE: PRODUCT PORTFOLIO
TABLE 076. SIFT-SCIENCE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 076. YOTTAMINE ANALYTICS: SNAPSHOT
TABLE 077. YOTTAMINE ANALYTICS: BUSINESS PERFORMANCE
TABLE 078. YOTTAMINE ANALYTICS: PRODUCT PORTFOLIO
TABLE 079. YOTTAMINE ANALYTICS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 079. ERSATZ LABS: SNAPSHOT
TABLE 080. ERSATZ LABS: BUSINESS PERFORMANCE
TABLE 081. ERSATZ LABS: PRODUCT PORTFOLIO
TABLE 082. ERSATZ LABS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 082. AMAZON WEB SERVICES: SNAPSHOT
TABLE 083. AMAZON WEB SERVICES: BUSINESS PERFORMANCE
TABLE 084. AMAZON WEB SERVICES: PRODUCT PORTFOLIO
TABLE 085. AMAZON WEB SERVICES: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 085. SAP SE: SNAPSHOT
TABLE 086. SAP SE: BUSINESS PERFORMANCE
TABLE 087. SAP SE: PRODUCT PORTFOLIO
TABLE 088. SAP SE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 088. OTHER MAJOR PLAYERS: SNAPSHOT
TABLE 089. OTHER MAJOR PLAYERS: BUSINESS PERFORMANCE
TABLE 090. OTHER MAJOR PLAYERS: PRODUCT PORTFOLIO
TABLE 091. OTHER MAJOR PLAYERS: KEY STRATEGIC MOVES AND DEVELOPMENTS

LIST OF FIGURES

FIGURE 001. YEARS CONSIDERED FOR ANALYSIS
FIGURE 002. SCOPE OF THE STUDY
FIGURE 003. MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY REGIONS
FIGURE 004. PORTER'S FIVE FORCES ANALYSIS
FIGURE 005. BARGAINING POWER OF SUPPLIERS
FIGURE 006. COMPETITIVE RIVALRYFIGURE 007. THREAT OF NEW ENTRANTS
FIGURE 008. THREAT OF SUBSTITUTES
FIGURE 009. VALUE CHAIN ANALYSIS
FIGURE 010. PESTLE ANALYSIS
FIGURE 011. MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COMPONENT
FIGURE 012. SOLUTIONS MARKET OVERVIEW (2016-2028)
FIGURE 013. SERVICES MARKET OVERVIEW (2016-2028)
FIGURE 014. MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY APPLICATION
FIGURE 015. NATURAL LANGUAGE PROCESSING MARKET OVERVIEW (2016-2028)
FIGURE 016. MARKETING & ADVERTISING MARKET OVERVIEW (2016-2028)
FIGURE 017. FRAUD DETECTION & RISK MANAGEMENT MARKET OVERVIEW (2016-2028)
FIGURE 018. PREDICTIVE ANALYTICS MARKET OVERVIEW (2016-2028)
FIGURE 019. OTHERS MARKET OVERVIEW (2016-2028)
FIGURE 020. MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY END-USE INDUSTRY
FIGURE 021. BFSI MARKET OVERVIEW (2016-2028)
FIGURE 022. GOVERNMENT & DEFENSE MARKET OVERVIEW (2016-2028)
FIGURE 023. HEALTHCARE & LIFE SCIENCES MARKET OVERVIEW (2016-2028)
FIGURE 024. TELECOMMUNICATIONS MARKET OVERVIEW (2016-2028)
FIGURE 025. RETAIL MARKET OVERVIEW (2016-2028)
FIGURE 026. OTHERS MARKET OVERVIEW (2016-2028)
FIGURE 027. NORTH AMERICA MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 028. EUROPE MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 029. ASIA PACIFIC MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 030. MIDDLE EAST & AFRICA MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 031. SOUTH AMERICA MACHINE LEARNING AS A SERVICE MARKET OVERVIEW BY COUNTRY (2016-2028)