INFORMATION & COMMUNICATION TECHNOLOGY
Global Machine Learning Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global Machine Learning Market, By Enterprise Size (SMEs and Large Enterprises), Vertical (Applications in BFSI, Applications in Healthcare and Life Sciences, Applications in Retail, Applications in Telecommunication, Applications in Government and Defense, Applications in Manufacturing, Applications in Energy and Utilities, and Others (Education, Agriculture, Media and Entertainment, and Education)), Service (Professional Services and Managed Services), Deployment (Cloud and On-Premise), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
Market Dynamics
- Increasing awareness and adoption of AI technologies
- Advancements in computing power
- Integration complexity
- Bias and ensuring algorithmic fairness
- Cross-industry collaboration and innovation
- Enhanced customer experience
- Data privacy and security
- Ethical and regulatory challenges
SEGMENTATION
- Enterprise Size
- SMEs
- Large Enterprises
- Vertical
- Applications in BFSI
- Fraud and Risk Management
- Investment Prediction
- Sales and Marketing Campaign Management
- Customer Segmentation
- Digital Assistance
- Others (Compliance Management and Credit Underwriting)
- Applications in Healthcare and Life Sciences
- Disease Identification and Diagnosis
- Image Analytics
- Drug Discovery/Manufacturing
- Others (Clinical Trial Research and Epidemic Outbreak Prediction)
- Applications in Retail
- Inventory Planning
- Upsell and Cross Channel Marketing
- Segmentation and Targeting
- Recommendation Engines
- Others (Customer ROI and Lifetime Value and Customization Management)
- Applications in Telecommunication
- Customer Analytics
- Network Optimization
- Network Security
- Others (Digital Assistance/ Contact Centers Analytics and Marketing Campaign Analytics)
- Applications in Government and Defense
- Threat Intelligence
- Autonomous Defense System
- Others
- Applications in Manufacturing
- Predictive Maintenance
- Demand Forecasting
- Revenue Estimation
- Supply Chain Management
- Others (Root Cause Analysis and Telematics)
- Applications in Energy and Utilities
- Power/Energy Usage Analytics
- Seismic Data Processing
- Smart Grid Management
- Carbon Emission
- Others (Customer Specific Pricing and Renewable Energy Management)
- Others (Education, Agriculture, Media and Entertainment, and Education)
- Service
- Professional Services
- Managed Services
- Deployment
- Cloud
- On-Premise
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- Amazon Web Services, Inc.
- Baidu Inc.
- Google Inc.
- Hewlett Packard Enterprise Development LP
- Intel Corporation
- International Business Machines Corporation
- Microsoft Corporation
- SAS Institute Inc.
- SAP SE
- RapidMiner, Inc.
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global Machine Learning Market, by Enterprise Size
- 3.2 Global Machine Learning Market, by Vertical
- 3.3 Global Machine Learning Market, by Service
- 3.4 Global Machine Learning Market, by Deployment
- 3.5 Global Machine Learning Market, by Geography
- 3.6 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Rise of explainable AI
- 5.1.2 Advancements in deep learning
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Increasing awareness and adoption of AI technologies
- 5.2.2 Advancements in computing power
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 Integration complexity
- 5.3.2 Bias and ensuring algorithmic fairness
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Cross-industry collaboration and innovation
- 5.4.2 Enhanced customer experience
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 Data privacy and security
- 5.5.2 Ethical and regulatory challenges
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL MACHINE LEARNING MARKET, BY ENTERPRISE SIZE
- 6.1 Enterprise Size Summary
- 6.2 Market Attractive Index
- 6.3 Global Machine Learning Market, by Enterprise Size (2019-2032)
- SECTION 7 - GLOBAL MACHINE LEARNING MARKET, BY VERTICAL
- 7.1 Vertical Summary
- 7.2 Market Attractive Index
- 7.3 Global Machine Learning Market, by Vertical (2019-2032)
- SECTION 8 - GLOBAL MACHINE LEARNING MARKET, BY SERVICE
- 8.1 Service Summary
- 8.2 Market Attractive Index
- 8.3 Global Machine Learning Market, by Service (2019-2032)
- SECTION 9 - GLOBAL MACHINE LEARNING MARKET, BY DEPLOYMENT
- 9.1 Deployment Summary
- 9.2 Market Attractive Index
- 9.3 Global Machine Learning Market, by Deployment (2019-2032)
- SECTION 10 - GLOBAL MACHINE LEARNING MARKET, BY GEOGRAPHY
- 10.1 Regional Summary
- 10.2 Market Attractive Index
- 10.3 Global Machine Learning Market, by Geography (2019-2032)
- SECTION 11 - NORTH AMERICA MACHINE LEARNING MARKET
- 11.1 North America Summary
- 11.2 Market Attractive Index
- 11.3 North America Machine Learning Market, by Enterprise Size (2019-2032)
- 11.4 North America Machine Learning Market, by Vertical (2019-2032)
- 11.5 North America Machine Learning Market, by Service (2019-2032)
- 11.6 North America Machine Learning Market, by Deployment (2019-2032)
- 11.7 North America Machine Learning Market, by Country (2019-2032)
- 11.7.1 U.S.
- 11.7.2 Canada
- 11.7.3 Mexico
- 11.7.4 Rest of North America
- SECTION 12 - EUROPE MACHINE LEARNING MARKET
- 12.1 Europe Summary
- 12.2 Market Attractive Index
- 12.3 Europe Machine Learning Market, by Enterprise Size (2019-2032)
- 12.4 Europe Machine Learning Market, by Vertical (2019-2032)
- 12.5 Europe Machine Learning Market, by Service (2019-2032)
- 12.6 Europe Machine Learning Market, by Deployment (2019-2032)
- 12.7 Europe Machine Learning Market, by Country (2019-2032)
- 12.7.1 Germany
- 12.7.2 U.K.
- 12.7.3 France
- 12.7.4 Italy
- 12.7.5 Spain
- 12.7.6 Russia
- 12.7.7 The Netherlands
- 12.7.8 Belgium
- 12.7.9 Turkey
- 12.7.10 Rest of Europe
- SECTION 13 - ASIA-PACIFIC MACHINE LEARNING MARKET
- 13.1 Asia-Pacific Summary
- 13.2 Market Attractive Index
- 13.3 Asia-Pacific Machine Learning Market, by Enterprise Size (2019-2032)
- 13.4 Asia-Pacific Machine Learning Market, by Vertical (2019-2032)
- 13.5 Asia-Pacific Machine Learning Market, by Service (2019-2032)
- 13.6 Asia-Pacific Machine Learning Market, by Deployment (2019-2032)
- 13.7 Asia-Pacific Machine Learning Market, by Country (2019-2032)
- 13.7.1 China
- 13.7.2 India
- 13.7.3 Japan
- 13.7.4 South Korea
- 13.7.5 Singapore
- 13.7.6 Malaysia
- 13.7.7 Australia
- 13.7.8 Thailand
- 13.7.9 Philippines
- 13.7.10 Rest of Asia-Pacific
- SECTION 14 - SOUTH AMERICA MACHINE LEARNING MARKET
- 14.1 South America Summary
- 14.2 Market Attractive Index
- 14.3 South America Machine Learning Market, by Enterprise Size (2019-2032)
- 14.4 South America Machine Learning Market, by Vertical (2019-2032)
- 14.5 South America Machine Learning Market, by Service (2019-2032)
- 14.6 South America Machine Learning Market, by Deployment (2019-2032)
- 14.7 South America Machine Learning Market, by Country (2019-2032)
- 14.7.1 Brazil
- 14.7.2 Argentina
- 14.7.3 Chile
- 14.7.4 Colombia
- 14.7.5 Rest of South America
- SECTION 15 - MIDDLE EAST AND AFRICA MACHINE LEARNING MARKET
- 15.1 Middle East and Africa Summary
- 15.2 Market Attractive Index
- 15.3 Middle East and Africa Machine Learning Market, by Enterprise Size (2019-2032)
- 15.4 Middle East and Africa Machine Learning Market, by Vertical (2019-2032)
- 15.5 Middle East and Africa Machine Learning Market, by Service (2019-2032)
- 15.6 Middle East and Africa Machine Learning Market, by Deployment (2019-2032)
- 15.7 Middle East and Africa Machine Learning Market, by Country (2019-2032)
- 15.7.1 Kingdom of Saudi Arabia
- 15.7.2 South Africa
- 15.7.3 U.A.E.
- 15.7.4 Egypt
- 15.7.5 Rest of Middle East and Africa
- SECTION 16 - COMPANY SHARE ANALYSIS
- 16.1 Global Machine Learning Market, Company Share Analysis
- 16.2 North America Machine Learning Market, Company Share Analysis
- 16.3 Europe Machine Learning Market, Company Share Analysis
- 16.4 Asia-Pacific Machine Learning Market, Company Share Analysis
- SECTION 17 - COMPANY PROFILES
- 17.1 Amazon Web Services, Inc.
- 17.1.1 Company Snapshot
- 17.1.2 Financial Overview
- 17.1.3 Product Portfolio
- 17.1.4 Recent Developments
- 17.2 Baidu Inc.
- 17.2.1 Company Snapshot
- 17.2.2 Financial Overview
- 17.2.3 Product Portfolio
- 17.2.4 Recent Developments
- 17.3 Google Inc.
- 17.3.1 Company Snapshot
- 17.3.2 Financial Overview
- 17.3.3 Product Portfolio
- 17.3.4 Recent Developments
- 17.4 Hewlett Packard Enterprise Development LP
- 17.4.1 Company Snapshot
- 17.4.2 Financial Overview
- 17.4.3 Product Portfolio
- 17.4.4 Recent Developments
- 17.5 Intel Corporation
- 17.5.1 Company Snapshot
- 17.5.2 Financial Overview
- 17.5.3 Product Portfolio
- 17.5.4 Recent Developments
- 17.6 International Business Machines Corporation
- 17.6.1 Company Snapshot
- 17.6.2 Financial Overview
- 17.6.3 Product Portfolio
- 17.6.4 Recent Developments
- 17.7 Microsoft Corporation
- 17.7.1 Company Snapshot
- 17.7.2 Financial Overview
- 17.7.3 Product Portfolio
- 17.7.4 Recent Developments
- 17.8 SAS Institute Inc.
- 17.8.1 Company Snapshot
- 17.8.2 Financial Overview
- 17.8.3 Product Portfolio
- 17.8.4 Recent Developments
- 17.9 SAP SE
- 17.9.1 Company Snapshot
- 17.9.2 Financial Overview
- 17.9.3 Product Portfolio
- 17.9.4 Recent Developments
- 17.10 RapidMiner, Inc.
- 17.10.1 Company Snapshot
- 17.10.2 Financial Overview
- 17.10.3 Product Portfolio
- 17.10.4 Recent Developments
- SECTION 18 - RELATED REPORTS
- SECTION 19 - DISCLAIMER
RESEARCH METHODOLOGY
RESEARCH AND DATA COLLECTION
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.