by According to Precedence Research, during the forecast period of 2022 to 2030, the global machine learning as a service market is estimated to develop at a compound annual growth rate (CAGR) of 39.3%. The global machine learning as a service market was valued at USD 15.47 billion in 2021, and it is predicted to exceed USD 305.62 billion by 2030. The study investigates several elements and their consequences on the growth of the machine learning as a service market.
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This report focuses on machine learning as a service market volume and value at the global level, regional level and company level. From a global perspective, this report represents overall machine learning as a service market size by analyzing historical data and future prospect. Regionally, this report focuses on several key regions: North America, Europe, Middle East & Africa, Latin America, etc.
Report Scope of the Machine Learning as a Service Market
| Report Coverage | Details |
| Market Size in 2022 | USD 21.55 Billion |
| Market Size by 2030 | USD 305.62 Billion |
| Growth Rate from 2022 to 2030 | CAGR of 39.3% |
| Base Year | 2021 |
| Forecast Period | 2022 to 2030 |
| Segments Covered | Component, Organization Size, Application, Industry Vertical, Geography |
The research report includes specific segments by region (country), by company, by all segments. This study provides information about the growth and revenue during the historic and forecasted period of 2017 to 2030. Understanding the segments helps in identifying the importance of different factors that aid the market growth.
In-Depth Analysis on Competitive Landscape
The report sheds light on leading manufacturers of machine learning as a service, along with their detailed profiles. Essential and up-to-date data related to market performers who are principally engaged in the production of machine learning as a service has been brought with the help of a detailed dashboard view. Market share analysis and comparison of prominent players provided in the report permits report readers to take preemptive steps in advancing their businesses.
Company profiles have been included in the report, which include essentials such as product portfolio, key strategies, along with all-inclusive SWOT analysis on each player. Company presence is mapped and presented through a matrix for all the prominent players, thus providing readers with actionable insights, which helps in thoughtfully presenting market status and predicting the competition level in the machine learning as a service market.
Some of the prominent players in the machine learning as a service market include:
- GOOGLE INC
- SAS INSTITUTE INC
- FICO
- HEWLETT PACKARD ENTERPRISE
- YOTTAMINE ANALYTICS
- AMAZON WEB SERVICES
- BIGML, INC
- MICROSOFT CORPORATION
- PREDICTRON LABS LTD
- IBM CORPORATION
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Segments Covered in the Report
By Component
- Solution
- Services
By Organization Size
- Small and Medium-Sized Enterprises
- Large Enterprises
By Application
- Marketing & Advertising
- Fraud Detection & Risk Management
- Computer vision
- Security & Surveillance
- Predictive analytics
- Natural Language Processing
- Augmented & Virtual Reality
- Others
By Industry Vertical
- BFSI
- IT & Telecom
- Automotive
- Healthcare
- Aerospace & Defense
- Retail
- Government
- Others
Regional Segmentation
- Asia-Pacific [China, Southeast Asia, India, Japan, Korea, Western Asia]
- Europe [Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland]
- North America [United States, Canada, Mexico]
- South America [Brazil, Argentina, Columbia, Chile, Peru]
- Middle East & Africa [GCC, North Africa, South Africa]
Some of the important ones are:
- What can be the best investment choices for venturing into new product and service lines?
- What value propositions should businesses aim at while making new research and development funding?
- Which regulations will be most helpful for stakeholders to boost their supply chain network?
- Which regions might see the demand maturing in certain segments in near future?
- What are the some of the best cost optimization strategies with vendors that some well-entrenched players have gained success with?
- Which are the key perspectives that the C-suite are leveraging to move businesses to new growth trajectory?
- Which government regulations might challenge the status of key regional markets?
- How will the emerging political and economic scenario affect opportunities in key growth areas?
- What are some of the value-grab opportunities in various segments?
- What will be the barrier to entry for new players in the market?
Table of Contents
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Machine Learning as a Service Market
5.1. COVID-19 Landscape: Machine Learning as a Service Industry Impact
5.2. COVID 19 – Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Machine Learning as a Service Market, By Component
8.1. Machine Learning as a Service Market, by Component, 2022-2030
8.1.1. Solution
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Services
8.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Machine Learning as a Service Market, By Organization Size
9.1. Machine Learning as a Service Market, by Organization Size e, 2022-2030
9.1.1. Small and Medium-Sized Enterprises
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Large Enterprises
9.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Machine Learning as a Service Market, By Application
10.1. Machine Learning as a Service Market, by Application, 2022-2030
10.1.1. Marketing & Advertising
10.1.1.1. Market Revenue and Forecast (2017-2030)
10.1.2. Fraud Detection & Risk Management
10.1.2.1. Market Revenue and Forecast (2017-2030)
10.1.3. Computer vision
10.1.3.1. Market Revenue and Forecast (2017-2030)
10.1.4. Security & Surveillance
10.1.4.1. Market Revenue and Forecast (2017-2030)
10.1.5. Predictive analytics
10.1.5.1. Market Revenue and Forecast (2017-2030)
10.1.6. Natural Language Processing
10.1.6.1. Market Revenue and Forecast (2017-2030)
10.1.7. Augmented & Virtual Reality
10.1.7.1. Market Revenue and Forecast (2017-2030)
10.1.8. Others
10.1.8.1. Market Revenue and Forecast (2017-2030)
Chapter 11. Global Machine Learning as a Service Market, By Industry Vertical
11.1. Machine Learning as a Service Market, by Industry Vertical, 2022-2030
11.1.1. BFSI
11.1.1.1. Market Revenue and Forecast (2017-2030)
11.1.2. IT & Telecom
11.1.2.1. Market Revenue and Forecast (2017-2030)
11.1.3. Automotive
11.1.3.1. Market Revenue and Forecast (2017-2030)
11.1.4. Healthcare
11.1.4.1. Market Revenue and Forecast (2017-2030)
11.1.5. Aerospace & Defense
11.1.5.1. Market Revenue and Forecast (2017-2030)
11.1.6. Retail
11.1.6.1. Market Revenue and Forecast (2017-2030)
11.1.7. Government
11.1.7.1. Market Revenue and Forecast (2017-2030)
11.1.8. Others
11.1.8.1. Market Revenue and Forecast (2017-2030)
Chapter 12. Global Machine Learning as a Service Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.1.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.1.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.1.6.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.1.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.1.6.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.2.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.2.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.6.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.2.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.6.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.7.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.2.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.7.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.2.8.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.2.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.2.8.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.3.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.3.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.6.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.3.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.6.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.7.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.3.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.7.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.3.8.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.3.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.3.8.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.4.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.4.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.6.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.4.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.6.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.7.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.4.7.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.7.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Component (2017-2030)
12.4.8.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.4.8.3. Market Revenue and Forecast, by Application (2017-2030)
12.4.8.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.5.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.5.5.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.5.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Component (2017-2030)
12.5.6.2. Market Revenue and Forecast, by Organization Size (2017-2030)
12.5.6.3. Market Revenue and Forecast, by Application (2017-2030)
12.5.6.4. Market Revenue and Forecast, by Industry Vertical (2017-2030)
Chapter 13. Company Profiles
13.1. GOOGLE INC
13.1.1. Company Overview
13.1.2. Product Offerings
13.1.3. Financial Performance
13.1.4. Recent Initiatives
13.2. SAS INSTITUTE INC
13.2.1. Company Overview
13.2.2. Product Offerings
13.2.3. Financial Performance
13.2.4. Recent Initiatives
13.3. FICO
13.3.1. Company Overview
13.3.2. Product Offerings
13.3.3. Financial Performance
13.3.4. Recent Initiatives
13.4. HEWLETT PACKARD ENTERPRISE
13.4.1. Company Overview
13.4.2. Product Offerings
13.4.3. Financial Performance
13.4.4. Recent Initiatives
13.5. YOTTAMINE ANALYTICS
13.5.1. Company Overview
13.5.2. Product Offerings
13.5.3. Financial Performance
13.5.4. Recent Initiatives
13.6. AMAZON WEB SERVICES
13.6.1. Company Overview
13.6.2. Product Offerings
13.6.3. Financial Performance
13.6.4. Recent Initiatives
13.7. BIGML, INC
13.7.1. Company Overview
13.7.2. Product Offerings
13.7.3. Financial Performance
13.7.4. Recent Initiatives
13.8. MICROSOFT CORPORATION
13.8.1. Company Overview
13.8.2. Product Offerings
13.8.3. Financial Performance
13.8.4. Recent Initiatives
13.9. PREDICTRON LABS LTD
13.9.1. Company Overview
13.9.2. Product Offerings
13.9.3. Financial Performance
13.9.4. Recent Initiatives
13.10. IBM
13.10.1. Company Overview
13.10.2. Product Offerings
13.10.3. Financial Performance
13.10.4. Recent Initiatives
Chapter 14. Research Methodology
14.1. Primary Research
14.2. Secondary Research
14.3. Assumptions
Chapter 15. Appendix
15.1. About Us
15.2. Glossary of Terms
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