Machine Learning Market by Vertical (BFSI, Healthcare and Life Sciences, Retail, Telecommunication, Government and Defense, Manufacturing, Energy and Utilities), Deployment Mode, Service, Organization Size, and Region - Global Forecast to 2022
The machine learning market is projected to grow at a CAGR of 44.1% during the forecast period
The machine learning market is expected to grow from USD 1.41 billion in 2017 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The proliferation of large and multidimensional data sets, rising focus towards solving real-time problems from data along with rising demand for sophisticated algorithm platform and tool is driving the adoption of machine learning across the globe.
The major issue in front of most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.
Professional service segment is expected to have a larger market share during the forecast period
The service segment in the machine learning market includes professional and managed services. Majority of the companies do not have the expertise to successfully manage infrastructure, and hence, they outsource these services to third-party partners to maintain the level of security and safety. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of machine learning solutions.
Large enterprises segment is expected to have a larger market size during the forecast period
The organization size segment in the machine learning market includes Small and Medium-Sized Enterprises (SMEs) and large enterprises. Emergence in the demand for cloud computing, cloud storage, IoT connected devices, and excessive use of smartphones are some of the prime reasons why large enterprises have turned toward machine learning for processing data. In large enterprises, machine learning has a huge potential for the big data technology in allowing precise decision-making for superior performance.
Asia Pacific (APAC) is expected to witness the highest growth rate during the forecast period
APAC is estimated to grow at the highest CAGR during the forecast period. Factors, such as continual growth in the mobile network, increasing the complexity of business, rise in demand for intelligent business processes, and exponential growth in data generation throughout the industry verticals are driving the machine learning market in the APAC region. The North American region is expected to have the largest market share during the forecast period. The major growth drivers for this region are the large-scale investments in implementing machine learning services due to the growth in demand for processed data. Moreover, recently the region also witnessed the widespread adoption of cloud-based machine learning platform among large enterprises and SMEs across multiple verticals.
In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people.
Research Coverage
The machine learning market has been segmented on the basis of verticals, deployment modes, organization sizes, services, and region. The machine learning is segmented on the basis of verticals into Banking, Financial Services, and Insurance (BFSI), energy and utilities, healthcare and life sciences, retail, telecommunication, manufacturing, government and defense, and others (transportation, agriculture, media and entertainment, and education). The verticals are further segmented on the basis of application areas, applications of machine learning in BFSI includes fraud and risk management, investment prediction, sales and marketing campaign management, customer segmentation, digital assistance, and others (compliance management and credit underwriting). Applications of machine learning in healthcare and life sciences includes disease identification and diagnosis, image analytics, drug discovery/manufacturing, personalized treatment, and others (clinical trial research and epidemic outbreak prediction). Applications of machine learning in retail includes inventory planning, upsell and cross channel marketing, segmentation and targeting, recommendation engines, and others (customer ROI and lifetime value and customization management). Applications of machine learning in telecommunication includes customer analytics, network optimization, network security, and others (digital assistance/contact centers analytics and marketing campaign analytics). Applications of machine learning in government and defense includes threat intelligence, autonomous defense system, and others (sustainability and operational analytics). Applications of machine learning in manufacturing includes predictive maintenance, demand forecasting, revenue estimation, supply chain management, and others (root cause analysis and telematics). Applications of machine learning in energy and utilities includes power/energy usage analytics, seismic data processing, smart grid management, carbon emission, and others (customer specific pricing and renewable energy management).
The services offered in the machine learning market include professional and managed services. The deployment modes in the machine learning market include the cloud and on-premises. The organization sizes are segmented into Small and Medium-Sized Enterprises (SMEs) and large enterprises. Finally, on the basis of regions, the machine learning market is segmented into North America, Europe, APAC, Middle East and Africa (MEA), and Latin America.
The report will help the market leaders and new entrants in the machine learning market in the following ways:
1. The report segments the market into various subsegments, hence it covers the market comprehensively. The report provides the closest approximations of the revenue numbers for the overall market and the subsegments. The market numbers are further split across different verticals and regions.
2. The report helps in understanding the overall growth of the market. It provides information on the key market drivers, restraints, challenges, and opportunities.
3. The report helps in understanding the competitors better and gaining more insights to strengthen the organization’s position in the market. The study also presents the positioning of the key players based on their product offerings and business strategies.
The machine learning market is expected to grow from USD 1.41 billion in 2017 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The proliferation of large and multidimensional data sets, rising focus towards solving real-time problems from data along with rising demand for sophisticated algorithm platform and tool is driving the adoption of machine learning across the globe.
The major issue in front of most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.
Professional service segment is expected to have a larger market share during the forecast period
The service segment in the machine learning market includes professional and managed services. Majority of the companies do not have the expertise to successfully manage infrastructure, and hence, they outsource these services to third-party partners to maintain the level of security and safety. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of machine learning solutions.
Large enterprises segment is expected to have a larger market size during the forecast period
The organization size segment in the machine learning market includes Small and Medium-Sized Enterprises (SMEs) and large enterprises. Emergence in the demand for cloud computing, cloud storage, IoT connected devices, and excessive use of smartphones are some of the prime reasons why large enterprises have turned toward machine learning for processing data. In large enterprises, machine learning has a huge potential for the big data technology in allowing precise decision-making for superior performance.
Asia Pacific (APAC) is expected to witness the highest growth rate during the forecast period
APAC is estimated to grow at the highest CAGR during the forecast period. Factors, such as continual growth in the mobile network, increasing the complexity of business, rise in demand for intelligent business processes, and exponential growth in data generation throughout the industry verticals are driving the machine learning market in the APAC region. The North American region is expected to have the largest market share during the forecast period. The major growth drivers for this region are the large-scale investments in implementing machine learning services due to the growth in demand for processed data. Moreover, recently the region also witnessed the widespread adoption of cloud-based machine learning platform among large enterprises and SMEs across multiple verticals.
In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people.
- By Company Type - Tier 1 - 18%, Tier 2 - 48%, and Tier 3 - 34%
- By Designation – C-level - 22%, Director-level - 43%, and Others - 35%
- By Region –North America - 42%, EMEA (Europe, Middle East and Africa) - 32%, and APAC (Asia Pacific) - 26%
Research Coverage
The machine learning market has been segmented on the basis of verticals, deployment modes, organization sizes, services, and region. The machine learning is segmented on the basis of verticals into Banking, Financial Services, and Insurance (BFSI), energy and utilities, healthcare and life sciences, retail, telecommunication, manufacturing, government and defense, and others (transportation, agriculture, media and entertainment, and education). The verticals are further segmented on the basis of application areas, applications of machine learning in BFSI includes fraud and risk management, investment prediction, sales and marketing campaign management, customer segmentation, digital assistance, and others (compliance management and credit underwriting). Applications of machine learning in healthcare and life sciences includes disease identification and diagnosis, image analytics, drug discovery/manufacturing, personalized treatment, and others (clinical trial research and epidemic outbreak prediction). Applications of machine learning in retail includes inventory planning, upsell and cross channel marketing, segmentation and targeting, recommendation engines, and others (customer ROI and lifetime value and customization management). Applications of machine learning in telecommunication includes customer analytics, network optimization, network security, and others (digital assistance/contact centers analytics and marketing campaign analytics). Applications of machine learning in government and defense includes threat intelligence, autonomous defense system, and others (sustainability and operational analytics). Applications of machine learning in manufacturing includes predictive maintenance, demand forecasting, revenue estimation, supply chain management, and others (root cause analysis and telematics). Applications of machine learning in energy and utilities includes power/energy usage analytics, seismic data processing, smart grid management, carbon emission, and others (customer specific pricing and renewable energy management).
The services offered in the machine learning market include professional and managed services. The deployment modes in the machine learning market include the cloud and on-premises. The organization sizes are segmented into Small and Medium-Sized Enterprises (SMEs) and large enterprises. Finally, on the basis of regions, the machine learning market is segmented into North America, Europe, APAC, Middle East and Africa (MEA), and Latin America.
The report will help the market leaders and new entrants in the machine learning market in the following ways:
1. The report segments the market into various subsegments, hence it covers the market comprehensively. The report provides the closest approximations of the revenue numbers for the overall market and the subsegments. The market numbers are further split across different verticals and regions.
2. The report helps in understanding the overall growth of the market. It provides information on the key market drivers, restraints, challenges, and opportunities.
3. The report helps in understanding the competitors better and gaining more insights to strengthen the organization’s position in the market. The study also presents the positioning of the key players based on their product offerings and business strategies.
1 INTRODUCTION
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.4 YEARS CONSIDERED FOR THE STUDY
1.5 CURRENCY
1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakdown of primaries
2.1.2.2 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.2 TOP-DOWN APPROACH
2.3 MICROQUADRANT RESEARCH METHODOLOGY
2.3.1 VENDOR INCLUSION CRITERIA
2.4 RESEARCH ASSUMPTIONS
2.5 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES IN THE MACHINE LEARNING MARKET
4.2 MACHINE LEARNING MARKET: TOP THREE VERTICALS
4.3 LIFECYCLE ANALYSIS, BY REGION, 2017–2022
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Technological advancements
5.2.1.2 Proliferation in data generation
5.2.2 RESTRAINTS
5.2.2.1 Lack of skilled employees
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing demand for intelligent business processes
5.2.3.2 Increasing adoption in modern applications
5.2.4 CHALLENGES
5.2.4.1 Sensitive data security
5.2.4.2 Ethical implications of the algorithms deployed
5.3 INDUSTRY TRENDS
5.3.1 MACHINE LEARNING: USE CASES
5.3.1.1 Introduction
5.3.1.2 USE CASE #1: Deliver analytics solution
5.3.1.3 USE CASE #2: Improve cross-selling capabilities
5.3.1.4 USE CASE #3: Increase revenue and decrease customer incompetence
5.3.1.5 USE CASE #4: Market basket analysis
5.4 MACHINE LEARNING PROCESS
5.5 REGULATORY IMPLICATIONS
5.5.1 INTRODUCTION
5.5.2 SARBANES-OXLEY ACT OF 2002
5.5.3 GENERAL DATA PROTECTION REGULATION (GDPR)
5.5.4 BASEL
6 MACHINE LEARNING MARKET ANALYSIS, BY VERTICAL
6.1 INTRODUCTION
6.1.1 MACHINE LEARNING APPLICATIONS IN BFSI
6.1.1.1 Fraud and risk management
6.1.1.2 Customer segmentation
6.1.1.3 Sales and marketing campaign management
6.1.1.4 Investment prediction
6.1.1.5 Digital assistance
6.1.1.6 Others
6.1.2 MACHINE LEARNING APPLICATIONS IN HEALTHCARE AND LIFE SCIENCES
6.1.2.1 Disease identification and diagnosis
6.1.2.2 Image analytics
6.1.2.3 Personalized treatment
6.1.2.4 Drug discovery/manufacturing
6.1.2.5 Others
6.1.3 MACHINE LEARNING APPLICATION IN RETAIL
6.1.3.1 Inventory planning
6.1.3.2 Recommendation engines
6.1.3.3 Upsells and cross channel marketing
6.1.3.4 Segmentation and targeting
6.1.3.5 Others
6.1.4 MACHINE LEARNING APPLICATIONS IN TELECOMMUNICATIONS
6.1.4.1 Customer analytics
6.1.4.2 Network security
6.1.4.3 Network optimization
6.1.4.4 Others
6.1.5 MACHINE LEARNING APPLICATIONS IN GOVERNMENT AND DEFENSE
6.1.5.1 Autonomous defense system
6.1.5.2 Threat intelligence
6.1.5.3 Others
6.1.6 MACHINE LEARNING APPLICATIONS IN MANUFACTURING
6.1.6.1 Predictive maintenance
6.1.6.2 Revenue estimation
6.1.6.3 Demand forecasting
6.1.6.4 Supply chain management
6.1.6.5 Others
6.1.7 MACHINE LEARNING APPLICATIONS IN ENERGY AND UTILITIES
6.1.7.1 Power/energy usage analytics
6.1.7.2 Seismic data processing
6.1.7.3 Carbon emission
6.1.7.4 Smart grid management
6.1.7.5 Others
6.1.8 OTHER APPLICATIONS
7 MACHINE LEARNING MARKET ANALYSIS, BY DEPLOYMENT MODE
7.1 INTRODUCTION
7.2 CLOUD
7.3 ON-PREMISES
8 MACHINE LEARNING MARKET ANALYSIS, BY ORGANIZATION SIZE
8.1 INTRODUCTION
8.2 LARGE ENTERPRISES
8.3 SMES
9 MACHINE LEARNING MARKET ANALYSIS, BY SERVICE
9.1 INTRODUCTION
9.2 PROFESSIONAL SERVICES
9.3 MANAGED SERVICES
10 GEOGRAPHIC ANALYSIS
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 BY VERTICAL
10.2.1.1 Machine learning applications in BFSI
10.2.1.2 Machine learning applications in healthcare and life sciences
10.2.1.3 Machine learning applications in retail
10.2.1.4 Machine learning applications in telecommunications
10.2.1.5 Machine learning applications in government and defense
10.2.1.6 Machine learning applications in manufacturing
10.2.1.7 Machine learning applications in energy and utilities
10.2.2 BY ORGANIZATION SIZE
10.2.3 BY DEPLOYMENT MODE
10.2.4 BY SERVICE
10.3 EUROPE
10.3.1 BY VERTICAL
10.3.1.1 Machine learning applications in BFSI
10.3.1.2 Machine learning applications in healthcare and life sciences
10.3.1.3 Machine learning applications in retail
10.3.1.4 Machine learning applications in telecommunications
10.3.1.5 Machine learning applications in government and defense
10.3.1.6 Machine learning applications in manufacturing
10.3.1.7 Machine learning applications in energy and utilities
10.3.2 BY ORGANIZATION SIZE
10.3.3 BY DEPLOYMENT MODE
10.3.4 BY SERVICE
10.4 APAC
10.4.1 BY VERTICAL
10.4.1.1 Machine learning applications in BFSI
10.4.1.2 Machine learning applications in healthcare and life sciences
10.4.1.3 Machine learning applications in retail
10.4.1.4 Machine learning applications in telecommunications
10.4.1.5 Machine learning applications in government and defense
10.4.1.6 Machine learning applications in manufacturing
10.4.1.7 Machine learning applications in energy and utilities
10.4.2 BY ORGANIZATION SIZE
10.4.3 BY DEPLOYMENT MODE
10.4.4 BY SERVICE
10.5 MEA
10.5.1 BY VERTICAL
10.5.1.1 Machine learning applications in BFSI
10.5.1.2 Machine learning applications in healthcare and life sciences
10.5.1.3 Machine learning applications in retail
10.5.1.4 Machine learning applications in telecommunications
10.5.1.5 Machine learning applications in government and defense
10.5.1.6 Machine learning applications in manufacturing
10.5.1.7 Machine learning applications in energy and utilities
10.5.2 BY ORGANIZATION SIZE
10.5.3 BY DEPLOYMENT MODE
10.5.4 BY SERVICE
10.6 LATIN AMERICA
10.6.1 BY VERTICAL
10.6.1.1 Machine learning applications in BFSI
10.6.1.2 Machine learning applications in healthcare and life sciences
10.6.1.3 Machine learning applications in retail
10.6.1.4 Machine learning applications in telecommunications
10.6.1.5 Machine learning applications in government and defense
10.6.1.6 Machine learning applications in manufacturing
10.6.1.7 Machine learning applications in energy and utilities
10.6.2 BY ORGANIZATION SIZE
10.6.3 BY DEPLOYMENT MODE
10.6.4 BY SERVICE
11 COMPETITIVE LANDSCAPE
11.1 MARKET RANKING FOR THE MACHINE LEARNING MARKET, 2017
12 COMPANY PROFILES
(Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*
12.1 INTERNATIONAL BUSINESS MACHINES CORPORATION
12.2 MICROSOFT CORPORATION
12.3 SAP SE
12.4 SAS INSTITUTE INC.
12.5 AMAZON WEB SERVICES, INC.
12.6 BIGML, INC.
12.7 GOOGLE INC.
12.8 FAIR ISAAC CORPORATION
12.9 BAIDU, INC.
12.10 HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
12.11 INTEL CORPORATION
12.12 H2O.AI
*Details on Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments might not be captured in case of unlisted companies.
13 APPENDIX
13.1 DISCUSSION GUIDE
13.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
13.3 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
13.4 AVAILABLE CUSTOMIZATIONS
13.5 RELATED REPORTS
13.6 AUTHOR DETAILS
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.4 YEARS CONSIDERED FOR THE STUDY
1.5 CURRENCY
1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakdown of primaries
2.1.2.2 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.2 TOP-DOWN APPROACH
2.3 MICROQUADRANT RESEARCH METHODOLOGY
2.3.1 VENDOR INCLUSION CRITERIA
2.4 RESEARCH ASSUMPTIONS
2.5 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES IN THE MACHINE LEARNING MARKET
4.2 MACHINE LEARNING MARKET: TOP THREE VERTICALS
4.3 LIFECYCLE ANALYSIS, BY REGION, 2017–2022
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Technological advancements
5.2.1.2 Proliferation in data generation
5.2.2 RESTRAINTS
5.2.2.1 Lack of skilled employees
5.2.3 OPPORTUNITIES
5.2.3.1 Increasing demand for intelligent business processes
5.2.3.2 Increasing adoption in modern applications
5.2.4 CHALLENGES
5.2.4.1 Sensitive data security
5.2.4.2 Ethical implications of the algorithms deployed
5.3 INDUSTRY TRENDS
5.3.1 MACHINE LEARNING: USE CASES
5.3.1.1 Introduction
5.3.1.2 USE CASE #1: Deliver analytics solution
5.3.1.3 USE CASE #2: Improve cross-selling capabilities
5.3.1.4 USE CASE #3: Increase revenue and decrease customer incompetence
5.3.1.5 USE CASE #4: Market basket analysis
5.4 MACHINE LEARNING PROCESS
5.5 REGULATORY IMPLICATIONS
5.5.1 INTRODUCTION
5.5.2 SARBANES-OXLEY ACT OF 2002
5.5.3 GENERAL DATA PROTECTION REGULATION (GDPR)
5.5.4 BASEL
6 MACHINE LEARNING MARKET ANALYSIS, BY VERTICAL
6.1 INTRODUCTION
6.1.1 MACHINE LEARNING APPLICATIONS IN BFSI
6.1.1.1 Fraud and risk management
6.1.1.2 Customer segmentation
6.1.1.3 Sales and marketing campaign management
6.1.1.4 Investment prediction
6.1.1.5 Digital assistance
6.1.1.6 Others
6.1.2 MACHINE LEARNING APPLICATIONS IN HEALTHCARE AND LIFE SCIENCES
6.1.2.1 Disease identification and diagnosis
6.1.2.2 Image analytics
6.1.2.3 Personalized treatment
6.1.2.4 Drug discovery/manufacturing
6.1.2.5 Others
6.1.3 MACHINE LEARNING APPLICATION IN RETAIL
6.1.3.1 Inventory planning
6.1.3.2 Recommendation engines
6.1.3.3 Upsells and cross channel marketing
6.1.3.4 Segmentation and targeting
6.1.3.5 Others
6.1.4 MACHINE LEARNING APPLICATIONS IN TELECOMMUNICATIONS
6.1.4.1 Customer analytics
6.1.4.2 Network security
6.1.4.3 Network optimization
6.1.4.4 Others
6.1.5 MACHINE LEARNING APPLICATIONS IN GOVERNMENT AND DEFENSE
6.1.5.1 Autonomous defense system
6.1.5.2 Threat intelligence
6.1.5.3 Others
6.1.6 MACHINE LEARNING APPLICATIONS IN MANUFACTURING
6.1.6.1 Predictive maintenance
6.1.6.2 Revenue estimation
6.1.6.3 Demand forecasting
6.1.6.4 Supply chain management
6.1.6.5 Others
6.1.7 MACHINE LEARNING APPLICATIONS IN ENERGY AND UTILITIES
6.1.7.1 Power/energy usage analytics
6.1.7.2 Seismic data processing
6.1.7.3 Carbon emission
6.1.7.4 Smart grid management
6.1.7.5 Others
6.1.8 OTHER APPLICATIONS
7 MACHINE LEARNING MARKET ANALYSIS, BY DEPLOYMENT MODE
7.1 INTRODUCTION
7.2 CLOUD
7.3 ON-PREMISES
8 MACHINE LEARNING MARKET ANALYSIS, BY ORGANIZATION SIZE
8.1 INTRODUCTION
8.2 LARGE ENTERPRISES
8.3 SMES
9 MACHINE LEARNING MARKET ANALYSIS, BY SERVICE
9.1 INTRODUCTION
9.2 PROFESSIONAL SERVICES
9.3 MANAGED SERVICES
10 GEOGRAPHIC ANALYSIS
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 BY VERTICAL
10.2.1.1 Machine learning applications in BFSI
10.2.1.2 Machine learning applications in healthcare and life sciences
10.2.1.3 Machine learning applications in retail
10.2.1.4 Machine learning applications in telecommunications
10.2.1.5 Machine learning applications in government and defense
10.2.1.6 Machine learning applications in manufacturing
10.2.1.7 Machine learning applications in energy and utilities
10.2.2 BY ORGANIZATION SIZE
10.2.3 BY DEPLOYMENT MODE
10.2.4 BY SERVICE
10.3 EUROPE
10.3.1 BY VERTICAL
10.3.1.1 Machine learning applications in BFSI
10.3.1.2 Machine learning applications in healthcare and life sciences
10.3.1.3 Machine learning applications in retail
10.3.1.4 Machine learning applications in telecommunications
10.3.1.5 Machine learning applications in government and defense
10.3.1.6 Machine learning applications in manufacturing
10.3.1.7 Machine learning applications in energy and utilities
10.3.2 BY ORGANIZATION SIZE
10.3.3 BY DEPLOYMENT MODE
10.3.4 BY SERVICE
10.4 APAC
10.4.1 BY VERTICAL
10.4.1.1 Machine learning applications in BFSI
10.4.1.2 Machine learning applications in healthcare and life sciences
10.4.1.3 Machine learning applications in retail
10.4.1.4 Machine learning applications in telecommunications
10.4.1.5 Machine learning applications in government and defense
10.4.1.6 Machine learning applications in manufacturing
10.4.1.7 Machine learning applications in energy and utilities
10.4.2 BY ORGANIZATION SIZE
10.4.3 BY DEPLOYMENT MODE
10.4.4 BY SERVICE
10.5 MEA
10.5.1 BY VERTICAL
10.5.1.1 Machine learning applications in BFSI
10.5.1.2 Machine learning applications in healthcare and life sciences
10.5.1.3 Machine learning applications in retail
10.5.1.4 Machine learning applications in telecommunications
10.5.1.5 Machine learning applications in government and defense
10.5.1.6 Machine learning applications in manufacturing
10.5.1.7 Machine learning applications in energy and utilities
10.5.2 BY ORGANIZATION SIZE
10.5.3 BY DEPLOYMENT MODE
10.5.4 BY SERVICE
10.6 LATIN AMERICA
10.6.1 BY VERTICAL
10.6.1.1 Machine learning applications in BFSI
10.6.1.2 Machine learning applications in healthcare and life sciences
10.6.1.3 Machine learning applications in retail
10.6.1.4 Machine learning applications in telecommunications
10.6.1.5 Machine learning applications in government and defense
10.6.1.6 Machine learning applications in manufacturing
10.6.1.7 Machine learning applications in energy and utilities
10.6.2 BY ORGANIZATION SIZE
10.6.3 BY DEPLOYMENT MODE
10.6.4 BY SERVICE
11 COMPETITIVE LANDSCAPE
11.1 MARKET RANKING FOR THE MACHINE LEARNING MARKET, 2017
12 COMPANY PROFILES
(Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*
12.1 INTERNATIONAL BUSINESS MACHINES CORPORATION
12.2 MICROSOFT CORPORATION
12.3 SAP SE
12.4 SAS INSTITUTE INC.
12.5 AMAZON WEB SERVICES, INC.
12.6 BIGML, INC.
12.7 GOOGLE INC.
12.8 FAIR ISAAC CORPORATION
12.9 BAIDU, INC.
12.10 HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
12.11 INTEL CORPORATION
12.12 H2O.AI
*Details on Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments might not be captured in case of unlisted companies.
13 APPENDIX
13.1 DISCUSSION GUIDE
13.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
13.3 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
13.4 AVAILABLE CUSTOMIZATIONS
13.5 RELATED REPORTS
13.6 AUTHOR DETAILS
LIST OF TABLES
Table 1 UNITED STATES DOLLAR EXCHANGE RATE, 2014–2016
Table 2 EVALUATION CRITERIA
Table 3 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
Table 4 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 5 BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 6 HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 7 RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 8 TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 9 GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 10 MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 11 ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 12 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 13 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 14 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 15 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 17 NORTH AMERICA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 18 NORTH AMERICA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 19 NORTH AMERICA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 20 NORTH AMERICA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 21 NORTH AMERICA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 22 NORTH AMERICA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 23 NORTH AMERICA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 24 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 25 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 26 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 27 EUROPE: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 28 EUROPE: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 29 EUROPE: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 30 EUROPE: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 31 EUROPE: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 32 EUROPE: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 33 EUROPE: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 34 EUROPE: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 35 EUROPE: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 36 EUROPE: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 EUROPE: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 38 APAC: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 39 APAC: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 40 APAC: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 41 APAC: MACHINE LEARNING MARKET SIZE IN RETAIL, BY APPLICATION, 2015–2022 (USD MILLION)
Table 42 APAC: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 43 APAC: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 44 APAC: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 APAC: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 46 APAC: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 47 APAC: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 48 APAC: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 49 MEA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 50 MEA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 51 MEA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 52 MEA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 53 MEA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 54 MEA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 55 MEA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 56 MEA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 57 MEA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 58 MEA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 59 MEA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 60 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 61 LATIN AMERICA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 62 LATIN AMERICA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 63 LATIN AMERICA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 64 LATIN AMERICA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 65 LATIN AMERICA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 66 LATIN AMERICA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 67 LATIN AMERICA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 68 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 69 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 70 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 71 MARKET RANKING FOR THE MACHINE LEARNING MARKET, 2017
Table 1 UNITED STATES DOLLAR EXCHANGE RATE, 2014–2016
Table 2 EVALUATION CRITERIA
Table 3 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
Table 4 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 5 BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 6 HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 7 RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 8 TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 9 GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 10 MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 11 ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 12 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 13 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 14 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 15 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 17 NORTH AMERICA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 18 NORTH AMERICA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 19 NORTH AMERICA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 20 NORTH AMERICA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 21 NORTH AMERICA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 22 NORTH AMERICA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 23 NORTH AMERICA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 24 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 25 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 26 NORTH AMERICA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 27 EUROPE: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 28 EUROPE: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 29 EUROPE: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 30 EUROPE: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 31 EUROPE: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 32 EUROPE: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 33 EUROPE: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 34 EUROPE: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 35 EUROPE: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 36 EUROPE: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 EUROPE: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 38 APAC: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 39 APAC: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 40 APAC: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 41 APAC: MACHINE LEARNING MARKET SIZE IN RETAIL, BY APPLICATION, 2015–2022 (USD MILLION)
Table 42 APAC: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 43 APAC: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 44 APAC: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 APAC: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 46 APAC: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 47 APAC: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 48 APAC: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 49 MEA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 50 MEA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 51 MEA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 52 MEA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 53 MEA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 54 MEA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 55 MEA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 56 MEA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 57 MEA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 58 MEA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 59 MEA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 60 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
Table 61 LATIN AMERICA: BFSI MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 62 LATIN AMERICA: HEALTHCARE AND LIFE SCIENCES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 63 LATIN AMERICA: RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 64 LATIN AMERICA: TELECOMMUNICATIONS MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 65 LATIN AMERICA: GOVERNMENT AND DEFENSE MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 66 LATIN AMERICA: MANUFACTURING MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 67 LATIN AMERICA: ENERGY AND UTILITIES MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 68 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
Table 69 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 70 LATIN AMERICA: MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 71 MARKET RANKING FOR THE MACHINE LEARNING MARKET, 2017
LIST OF FIGURES
Figure 1 GLOBAL MACHINE LEARNING MARKET: MARKET SEGMENTATION
Figure 2 GLOBAL MACHINE LEARNING MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY SIZE, DESIGNATION, AND REGION
Figure 4 DATA TRIANGULATION
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 7 MACHINE LEARNING MARKET, BY VERTICAL, 2017 (USD MILLION)
Figure 8 MACHINE LEARNING MARKET, BY BFSI APPLICATION, 2017 (USD MILLION)
Figure 9 MACHINE LEARNING MARKET SNAPSHOT, BY HEALTHCARE AND LIFE SCIENCES APPLICATION, 2017 (USD MILLION)
Figure 10 MACHINE LEARNING MARKET, BY RETAIL APPLICATION, 2017 (USD MILLION)
Figure 11 MACHINE LEARNING MARKET, BY TELECOMMUNICATIONS APPLICATION, 2017 (USD MILLION)
Figure 12 MACHINE LEARNING MARKET, BY GOVERNMENT AND DEFENSE APPLICATION, 2017 (USD MILLION)
Figure 13 MACHINE LEARNING MARKET, BY MANUFACTURING APPLICATION, 2017 (USD MILLION)
Figure 14 MACHINE LEARNING MARKET, BY ENERGY AND UTILITIES APPLICATION, 2017 (USD MILLION)
Figure 15 MACHINE LEARNING MARKET, BY SERVICE (MARKET SHARE 2017)
Figure 16 MACHINE LEARNING MARKET, BY ORGANIZATION SIZE (MARKET SHARE 2017)
Figure 17 MACHINE LEARNING MARKET, BY DEPLOYMENT MODE (MARKET SHARE 2017)
Figure 18 MACHINE LEARNING MARKET, BY REGION
Figure 19 PROLIFERATION IN DATA GENERATION ONE OF THE MAJOR FACTORS DRIVING THE MACHINE LEARNING MARKET DURING THE FORECAST PERIOD
Figure 20 HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 21 APAC TO EXHIBIT THE HIGHEST GROWTH POTENTIAL DURING THE FORECAST PERIOD
Figure 22 APAC TO BE THE BEST MARKET FOR INVESTMENT IN THE NEXT 5 YEARS
Figure 23 MACHINE LEARNING MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 24 MACHINE LEARNING PROCESS
Figure 25 HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 26 FRAUD AND RISK MANAGEMENT APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 27 DISEASE IDENTIFICATION AND DIAGNOSIS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 28 INVENTORY PLANNING APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 29 CUSTOMER ANALYTICS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 30 THREAT INTELLIGENCE APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 31 PREDICTIVE MAINTENANCE APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 32 POWER/ENERGY USAGE ANALYTICS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 33 CLOUD DEPLOYMENT MODE TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 34 SMES SEGMENT TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 35 MANAGED SERVICES SEGMENT TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 36 NORTH AMERICA IS EXPECTED TO HOLD THE LARGEST MARKET SIZE DURING THE FORECAST PERIOD
Figure 37 APAC TO RECORD HIGHEST GROWTH RATE IN THE MACHINE LEARNING MARKET, 2017-2022
Figure 38 NORTH AMERICA: MARKET SNAPSHOT
Figure 39 NORTH AMERICA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 40 MAJOR FINTECH COMPANIES IN NORTH AMERICA USING MACHINE LEARNING
Figure 41 EUROPE: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 42 APAC: MARKET SNAPSHOT
Figure 43 APAC: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 44 MEA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 45 LATIN AMERICA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 46 INTERNATIONAL BUSINESS MACHINES CORPORATION: COMPANY SNAPSHOT
Figure 47 MICROSOFT CORPORATION: COMPANY SNAPSHOT
Figure 48 SAP SE: COMPANY SNAPSHOT
Figure 49 AMAZON WEB SERVICES, INC.: COMPANY SNAPSHOT
Figure 50 GOOGLE INC.: COMPANY SNAPSHOT
Figure 51 FAIR ISAAC CORPORATION: COMPANY SNAPSHOT
Figure 52 BAIDU, INC.: COMPANY SNAPSHOT
Figure 53 HPE DEVELOPMENT LP: COMPANY SNAPSHOT
Figure 54 INTEL CORPORATION: COMPANY SNAPSHOT
Figure 1 GLOBAL MACHINE LEARNING MARKET: MARKET SEGMENTATION
Figure 2 GLOBAL MACHINE LEARNING MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY SIZE, DESIGNATION, AND REGION
Figure 4 DATA TRIANGULATION
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 7 MACHINE LEARNING MARKET, BY VERTICAL, 2017 (USD MILLION)
Figure 8 MACHINE LEARNING MARKET, BY BFSI APPLICATION, 2017 (USD MILLION)
Figure 9 MACHINE LEARNING MARKET SNAPSHOT, BY HEALTHCARE AND LIFE SCIENCES APPLICATION, 2017 (USD MILLION)
Figure 10 MACHINE LEARNING MARKET, BY RETAIL APPLICATION, 2017 (USD MILLION)
Figure 11 MACHINE LEARNING MARKET, BY TELECOMMUNICATIONS APPLICATION, 2017 (USD MILLION)
Figure 12 MACHINE LEARNING MARKET, BY GOVERNMENT AND DEFENSE APPLICATION, 2017 (USD MILLION)
Figure 13 MACHINE LEARNING MARKET, BY MANUFACTURING APPLICATION, 2017 (USD MILLION)
Figure 14 MACHINE LEARNING MARKET, BY ENERGY AND UTILITIES APPLICATION, 2017 (USD MILLION)
Figure 15 MACHINE LEARNING MARKET, BY SERVICE (MARKET SHARE 2017)
Figure 16 MACHINE LEARNING MARKET, BY ORGANIZATION SIZE (MARKET SHARE 2017)
Figure 17 MACHINE LEARNING MARKET, BY DEPLOYMENT MODE (MARKET SHARE 2017)
Figure 18 MACHINE LEARNING MARKET, BY REGION
Figure 19 PROLIFERATION IN DATA GENERATION ONE OF THE MAJOR FACTORS DRIVING THE MACHINE LEARNING MARKET DURING THE FORECAST PERIOD
Figure 20 HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 21 APAC TO EXHIBIT THE HIGHEST GROWTH POTENTIAL DURING THE FORECAST PERIOD
Figure 22 APAC TO BE THE BEST MARKET FOR INVESTMENT IN THE NEXT 5 YEARS
Figure 23 MACHINE LEARNING MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 24 MACHINE LEARNING PROCESS
Figure 25 HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 26 FRAUD AND RISK MANAGEMENT APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 27 DISEASE IDENTIFICATION AND DIAGNOSIS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 28 INVENTORY PLANNING APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 29 CUSTOMER ANALYTICS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 30 THREAT INTELLIGENCE APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 31 PREDICTIVE MAINTENANCE APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 32 POWER/ENERGY USAGE ANALYTICS APPLICATION TO HOLD LARGEST MARKET SIZE, 2017-2022 (USD MILLION)
Figure 33 CLOUD DEPLOYMENT MODE TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 34 SMES SEGMENT TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 35 MANAGED SERVICES SEGMENT TO EXHIBIT HIGHER CAGR, 2017-2022 (USD MILLION)
Figure 36 NORTH AMERICA IS EXPECTED TO HOLD THE LARGEST MARKET SIZE DURING THE FORECAST PERIOD
Figure 37 APAC TO RECORD HIGHEST GROWTH RATE IN THE MACHINE LEARNING MARKET, 2017-2022
Figure 38 NORTH AMERICA: MARKET SNAPSHOT
Figure 39 NORTH AMERICA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 40 MAJOR FINTECH COMPANIES IN NORTH AMERICA USING MACHINE LEARNING
Figure 41 EUROPE: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 42 APAC: MARKET SNAPSHOT
Figure 43 APAC: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 44 MEA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 45 LATIN AMERICA: HEALTHCARE AND LIFE SCIENCES VERTICAL TO RECORD HIGHEST CAGR, 2017-2022 (USD MILLION)
Figure 46 INTERNATIONAL BUSINESS MACHINES CORPORATION: COMPANY SNAPSHOT
Figure 47 MICROSOFT CORPORATION: COMPANY SNAPSHOT
Figure 48 SAP SE: COMPANY SNAPSHOT
Figure 49 AMAZON WEB SERVICES, INC.: COMPANY SNAPSHOT
Figure 50 GOOGLE INC.: COMPANY SNAPSHOT
Figure 51 FAIR ISAAC CORPORATION: COMPANY SNAPSHOT
Figure 52 BAIDU, INC.: COMPANY SNAPSHOT
Figure 53 HPE DEVELOPMENT LP: COMPANY SNAPSHOT
Figure 54 INTEL CORPORATION: COMPANY SNAPSHOT
