July 20, 2024

Machine Learning as a Service Market size is Expected to Surpass Around US$ 305.62 Billion by 2030

[150+ Pages Report] As per the latest Research and survey report issued by Precedence Research, the global machine learning as a service market was valued at around USD 15.47 billion in 2021 and is expected to register revenues worth USD 305.62 billion by the end of 2030, growing at an exceptional CAGR of approximately 39.3% between 2022 and 2030.

Machine Learning as a Service

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Machine Learning as a Service Market Report Scope

A recent study by Precedence Research on the machine learning as a service market offers a forecast for 2022 and 2030. The study analyzes crucial trends that are currently determining the growth of the machine learning as a service market. This report explicates on vital dynamics such as the drivers, restraints, and opportunities for key market players, along with key stakeholders as well as emerging players associated with the manufacturing of machine learning as a service. The study also provides the dynamics that are responsible for influencing the future status of the machine learning as a service market over the forecast period.

A detailed assessment of the machine learning as a service market value chain analysis, business execution, and supply chain analysis across regional markets has been covered in the report. A list of prominent companies operating in the machine learning as a service market along with their product portfolio enhances the reliability of this comprehensive research study.

Competition Landscape

The report has engulfed a chapter on the global machine learning as a service market’s competitive landscape, which provides detailed analysis and insights on companies offering machine learning as a service. Profiles of key companies, along with a strategic overview of their M&A and expansion plans across geographies, have been delivered in this chapter. This chapter is priceless for report readers, as its enables them in gauging their growth potential in the market and implement key strategies for extending their market reach.

This chapter offers key recommendations for both new and existing market participants, enabling them to emerge sustainably and profitably. Intelligence on the market players has been delivered on the basis of their product overview, SWOT analysis, key developments, key financials and company overview. Occupancy of these market participants has been tracked by the report and portrayed via an intensity map.

Read Also@ Micro Motor Market Size to Worth Around US$ 61.27 Billion by 2030

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
Machine Learning as a Service Market Segmentation

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]

Regional Analysis

The research report includes a detailed study of regions of North America, Europe, China, Japan and Rest of the World. The report has been curated after observing and studying various factors that determine regional growth such as economic, environmental, social, technological, and political status of the particular region. Analysts have studied the data of revenue and manufacturers of each region. This section analyses region-wise revenue and volume for the forecast period of 2022 to 2030. These analyses will help the reader to understand the potential worth of investment in a particular region.

The report provides in-depth segment analysis of the global machine learning as a service market, thereby providing valuable insights at macro as well as micro levels. Analysis of major countries, which hold growth opportunities or account for significant share has also been included as part of geographic analysis of the machine learning as a service market.

The report includes country-wise and region-wise market size for the period 2022-2030. It also includes market size and forecast by segments in terms of production capacity, price and revenue for the period 2022-2030.

Why should you invest in this report?

If you are aiming to enter the global machine learning as a service market, this report is a comprehensive guide that provides crystal clear insights into this niche market. All the major application areas for machine learning as a service are covered in this report and information is given on the important regions of the world where this market is likely to boom during the forecast period of 2022-2030 so that you can plan your strategies to enter this market accordingly.

Besides, through this report, you can have a complete grasp of the level of competition you will be facing in this hugely competitive market and if you are an established player in this market already, this report will help you gauge the strategies that your competitors have adopted to stay as market leaders in this market. For new entrants to this market, the voluminous data provided in this report is invaluable.

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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Janet Edward

I have completed my education in Bachelors in Computer Application. A focused learner having a keen interest in the field of digital marketing, SEO, SMM, and Google Analytics enthusiastic to learn new things along with building leadership skills.

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