The global machine learning market was valued at USD 1.7 billion in 2018 and is expected to reach USD 33.4 billion in 2026, growing at a CAGR of 44.3% during the forecast period.
Machine learning is a branch of artificial intelligence that can allow computers to learn without being programmed in detail. It is working on the advancement of computer programs that can be switched when exposed to new data.
The growth of the machine learning market is primarily driven by growing demand for cloud-based services, growing concerns in safety and security, and growing advancements in technologies. Moreover, rising demand from growing end-use industries, such as Banking, Financial Services, and Insurance (BFSI), manufacturing, telecommunication, energy and utilities, and retail for developing complex business processes with improved efficiency and lowering the overall costs in emerging countries also drives the growth of the global machine learning market. On the other hand, lack of technical expertise and government and compliance issues is creating a major hindrance to its market growth. However, continuous development in IoT platforms and growing connectivity and communication technologies is expected to create major growth opportunities during the forecast period.
Machine Learning Market Segmentation |
|
| By Service | 1. Professional Services |
| 2. Managed Services | |
| By Deployment Mode | 1. Cloud |
| 2. On-Premise | |
| By Organization Size | 1. Large Enterprises |
| 2. SMEs | |
| By Industry Vertical | 1. Healthcare and Life Sciences |
| 2. Banking, Financial Services, And Insurance (BFSI) | |
| 3. Retail | |
| 4. Telecommunication | |
| 5. Government and Defense | |
| 6. Manufacturing | |
| 7. Energy | |
| 8. Utilities | |
| By Region | 1. North America (US and Canada) |
| 2. Europe (UK, Germany, France and Rest of Europe) | |
| 3. Asia Pacific (China, Japan, India and Rest of Asia Pacific) | |
| 4. Latin America (Brazil, Mexico and Rest of Latin America) | |
| 5. Middle East & Africa (GCC and Rest of Middle East & Africa) | |
The managed service segment expected to grow at the fastest rate of 51% CAGR during the forecast period
Based on service, the global machine learning market has been segmented into professional services and managed services. The managed services segment is expected to grow at the highest CAGR of around 51% during the forecast period. Due to it helps the various offices and organizations to increase efficiency and save costs for managing on-demand machine learning services. The increase in deployment of machine learning solutions and the complexity of operations further expected to grow the demand for managed services.
Based on deployment mode, the cloud deployment mode segment is expected to lead during the forecast period
Based on deployment mode, the market has been segmented into cloud and on-premises. The cloud deployment mode segment accounted for the largest share of around 64% in the market in 2018. The cloud-based services offer various benefits which include automated software updates, increased collaboration, data loss prevention, automated software updates, and cloud storage facilities for machine learning software solutions and services. Moreover, the manufacturing process of machine learning paints & coatings requires a lower carbon footprint as they consume less energy.
Based on organization size, the large enterprises segment is expected to lead during the forecast period
Based on organization size, the market has been segmented into large enterprises and SMEs. The large enterprise's segment accounted for the largest share of 55% in terms of value in the market in 2018. The large organization uses machine learning to extract the required information from a large amount of data and forecast the outcome of various problems. Moreover, growing digitization and increased cyber risks to critical business information and data further grows the demand for machine learning in large enterprises during the forecast period.
Based on vertical, the banking, financial services, and insurance (BFSI) segment is expected to lead during the forecast period
Based on vertical, the market has been segmented into healthcare and life sciences, banking, financial services, and insurance (BFSI), retail, telecommunication, government and defense, manufacturing, energy, and utilities. The banking, financial services, and insurance (BFSI) segment accounted for the largest share of 28% in the market in 2018. The Banking, Financial Services, and Insurance (BFSI) generate an ample amount of data every second and it requires appropriate data management with enhanced accuracy and extract business-critical insights from this ever-increasing data. Moreover, growing demand for managing the complex business processes with improved efficiency and lowering the overall costs further grows the demand for machine learning in Banking, Financial Services, and Insurance (BFSI) segment during the forecast period.
North America to dominate the machine learning market throughout the forecast period
North America accounted for nearly 32% share of the global machine learning market in 2018 and is expected to dominate the market throughout the forecast period followed by Europe and Asia-Pacific. The most competitive and rapidly changing market along with strong economic growth in the North America region is one of the key factors driving the growth of machine learning in this region. Moreover, a high focus on innovations obtained from R&D and awareness for business productivity in key countries such as the US and Canada are also propelling the growth of machine learning in the North America region. Asia-Pacific accounted for the highest CAGR in the global machine learning market during the forecast period.
Company Profiles and Competitive Intelligence:
The major players operating in the global machine learning market are Amazon Web Services (US), Google, Inc. (US), Microsoft (US), Hewlett Packard Enterprises (US), IBM Corporation (US), Intel Corporation (US), AT&T (US), Yottamine Analytics (US), BigML Inc. (US), and Ersatz Labs (US) are among others.
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TABLE OF CONTENT
Chapter 1 Executive Summary
1.1. Market Summary
1.1.1. Global Machine Learning Market, 2016-2026, (USD Million)
1.2. Market Snapshot: Global Machine Learning Market
1.3. Market Dynamics
1.4. Global Machine Learning Market, by Segment, 2018
1.4.1. Global Machine Learning Market, by Service, 2018, (USD Million)
1.4.2. Global Machine Learning Market, by Deployment Mode, 2018, (USD Million)
1.4.3. Global Machine Learning Market, by Organization Size, 2018, (USD Million)
1.4.4. Global Machine Learning Market, by Industry Vertical, 2018, (USD Million)
1.4.5. Global Machine Learning Market, by Region, 2018 (USD Million)
1.5. Premium Insights
1.5.1. Machine Learning Market In Developed Vs. Developing Economies, 2018 vs 2026
1.5.2. Global Machine Learning Market: Regional Life Cycle Analysis
Chapter 2 Market Dynamics
2.1. Market Overview
2.2. Market Drivers
2.2.1. Growing demand for cloud-based services
2.2.2. Growing advancements in technologies
2.3. Market Restraints
2.3.1. Lack of technical expertise
2.3.2. Government and compliance issues
2.4. Market Opportunities
2.4.1. Continuous development in IoT platforms
2.5. Industry Value Chain Analysis
2.6. Pricing Analysis
2.7. Porter’s Five Forces Analysis
Chapter 3 Global Machine Learning Market, by Service
3.1. Market Overview, by Service
3.1.1. Global Machine Learning Market, by Country, 2016-2026 (USD Million)
3.1.2. Incremental Opportunity, by Service, 2018
3.2. Managed Services
3.2.1. Global Machine Learning Market, by Managed Services, 2016-2026, (USD Million)
3.3. Professional Services
3.3.1. Global Machine Learning Market, by Professional Services, 2016-2026, (USD Million)
Chapter 4 Global Machine Learning Market, by Deployment Mode
4.1. Market Overview, by Deployment Mode
4.1.1. Global Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
4.1.2. Incremental Opportunity, by Deployment Mode, 2018
4.2. Cloud
4.2.1. Global Machine Learning Market, by Cloud, 2016-2026, (USD Million)
4.3. On-Premises
4.3.1. Global Machine Learning Market, by On-Premises, 2016-2026, (USD Million)
Chapter 5 Global Machine Learning Market, by Organization Size
5.1. Market Overview, by Organization Size
5.1.1. Global Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
5.1.2. Incremental Opportunity, by Organization Size, 2018
5.2. Large Enterprises
5.2.1. Global Machine Learning Market, by Large Enterprises, 2016-2026, (USD Million)
5.3. SMEs
5.3.1. Global Machine Learning Market, by SMEs, 2016-2026, (USD Million)
Chapter 6 Global Machine Learning Market, by Industry Vertical
6.1. Market Overview, by Industry Vertical
6.1.1. Global Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
6.1.2. Incremental Opportunity, by Industry Vertical, 2018
6.2. BFSI
6.2.1. Global Machine Learning Market, by BFSI, 2016-2026, (USD Million)
6.3. Healthcare and Life Sciences
6.3.1. Global Machine Learning Market, by Healthcare and Life Sciences, 2016-2026, (USD Million)
6.4. Retail
6.4.1. Global Machine Learning Market, by Retail, 2016-2026, (USD Million)
6.5. Telecommunications
6.5.1. Global Machine Learning Market, by Telecommunications, 2016-2026, (USD Million)
6.6. Government and Defense
6.6.1. Global Machine Learning Market, by Government and Defense, 2016-2026, (USD Million)
6.7. Manufacturing
6.7.1. Global Machine Learning Market, by Manufacturing, 2016-2026, (USD Million)
6.8. Energy and Utilities
6.8.1. Global Machine Learning Market, by Energy and Utilities, 2016-2026, (USD Million)
6.9. Other
6.9.1. Global Machine Learning Market, by Other, 2016-2026, (USD Million)
Chapter 7 Global Machine Learning Market, by Region
7.1. Market Overview, by Region
7.1.1. Global Machine Learning Market, by Region, 2016-2026, (USD Million)
7.2. Attractive Investment Opportunity, by Region, 2018
7.3. North America Machine Learning Market
7.3.1. North America Machine Learning Market, by Service, 2016-2026 (USD Million)
7.3.2. North America Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
7.3.3. North America Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
7.3.4. North America Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
7.3.5. United States Machine Learning Market, 2016-2026 (USD Million)
7.3.6. Canada Machine Learning Market, 2016-2026 (USD Million)
7.4. Europe Machine Learning Market
7.4.1. Europe Machine Learning Market, by Service, 2016-2026 (USD Million)
7.4.2. Europe Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
7.4.3. Europe Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
7.4.4. Europe Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
7.4.5. United Kingdom Machine Learning Market, 2016-2026 (USD Million)
7.4.6. Germany Machine Learning Market, 2016-2026 (USD Million)
7.4.7. France Machine Learning Market, 2016-2026 (USD Million)
7.4.8. Rest of Europe Machine Learning Market, 2016-2026 (USD Million)
7.5. Asia Pacific Machine Learning Market
7.5.1. Asia Pacific Machine Learning Market, by Service, 2016-2026 (USD Million)
7.5.2. Asia Pacific Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
7.5.3. Asia Pacific Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
7.5.4. Asia Pacific Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
7.5.5. China Machine Learning Market, 2016-2026 (USD Million)
7.5.6. Japan Machine Learning Market, 2016-2026 (USD Million)
7.5.7. India Machine Learning Market, 2016-2026 (USD Million)
7.5.8. Rest of Asia Pacific Machine Learning Market, 2016-2026 (USD Million)
7.6. Latin America Machine Learning Market
7.6.1. Latin America Machine Learning Market, by Service, 2016-2026 (USD Million)
7.6.2. Latin America Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
7.6.3. Latin America Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
7.6.4. Latin America Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
7.6.5. Brazil Machine Learning Market, 2016-2026 (USD Million)
7.6.6. Mexico Machine Learning Market, 2016-2026 (USD Million)
7.6.7. Rest of Latin America Machine Learning Market, 2016-2026 (USD Million)
7.7. Middle East & Africa Machine Learning Market
7.7.1. Middle East & Africa Machine Learning Market, by Service, 2016-2026 (USD Million)
7.7.2. Middle East & Africa Machine Learning Market, by Deployment Mode, 2016-2026 (USD Million)
7.7.3. Middle East & Africa Machine Learning Market, by Organization Size, 2016-2026 (USD Million)
7.7.4. Middle East & Africa Machine Learning Market, by Industry Vertical, 2016-2026 (USD Million)
7.7.5. GCC Machine Learning Market, 2016-2026 (USD Million)
7.7.6. Rest of Middle East & Africa Machine Learning Market, 2016-2026 (USD Million)
Chapter 8 Competitive Intelligence
8.1. Top 5 Players Comparison
8.2. Market Positioning of Key Players, 2018
8.3. Market Players Mapping
8.3.1. By Service
8.3.2. By Deployment Mode
8.3.3. By Organization Size
8.3.4. By Vertical
8.3.5. By Region
8.4. Strategies Adopted by Key Market Players
8.5. Recent Developments in the Market
8.5.1. Mergers & Acquisitions, Partnership, New Service Developments
Chapter 9 Company Profiles
9.1. Amazon Web Services
9.1.1. Amazon Web Services Overview
9.1.2. Amazon Web Services Services Portfolio
9.1.3. Amazon Web Services Financial Overview
9.1.4. Amazon Web Services News/Recent Developments
9.2. Google, Inc.
9.2.1. Google, Inc. Overview
9.2.2. Google, Inc. Services Portfolio
9.2.3. Google, Inc. Financial Overview
9.2.4. Google, Inc. News/Recent Developments
9.3. MICROSOFT
9.3.1. MICROSOFT Overview
9.3.2. MICROSOFT Services Portfolio
9.3.3. MICROSOFT Financial Overview
9.3.4. MICROSOFT News/Recent Developments
9.4. Hewlett Packard Enterprises
9.4.1. Hewlett Packard Enterprises Overview
9.4.2. Hewlett Packard Enterprises Services Portfolio
9.4.3. Hewlett Packard Enterprises Financial Overview
9.4.4. Hewlett Packard Enterprises News/Recent Developments
9.5. IBM Corporation
9.5.1. IBM Corporation Overview
9.5.2. IBM Corporation Services Portfolio
9.5.3. IBM Corporation Financial Overview
9.5.4. IBM Corporation News/Recent Developments
9.6. Intel Corporation
9.6.1. Intel Corporation Overview
9.6.2. Intel Corporation Services Portfolio
9.6.3. Intel Corporation Financial Overview
9.6.4. Intel Corporation News/Recent Developments
9.7. AT&T
9.7.1. AT&T Overview
9.7.2. AT&T Services Portfolio
9.7.3. AT&T Financial Overview
9.7.4. AT&T News/Recent Developments
9.8. Yottamine Analytics
9.8.1. Yottamine Analytics Overview
9.8.2. Yottamine Analytics Services Portfolio
9.8.3. Yottamine Analytics Financial Overview
9.8.4. Yottamine Analytics News/Recent Developments
9.9. BigML Inc.
9.9.1. BigML Inc. Overview
9.9.2. BigML Inc. Services Portfolio
9.9.3. BigML Inc. Financial Overview
9.9.4. BigML Inc. News/Recent Developments
9.10. Ersatz Labs
9.10.1. Ersatz Labs Overview
9.10.2. Ersatz Labs Services Portfolio
9.10.3. Ersatz Labs Financial Overview
9.10.4. Ersatz Labs News/Recent Developments
Chapter 10 Preface
10.1. Data Triangulation
10.2. Research Methodology
10.2.1. Phase I – Secondary Research
10.2.2. Phase II – Primary Research
10.2.3. Phase III – Expert Panel Review
10.2.4. Approach Adopted
10.2.4.1. Top-Down Approach
10.2.4.2. Bottom-Up Approach
10.2.5. Supply- Demand side
10.2.6. Breakup of the Primary Profiles
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