Machine Learning Market Size, Share, and Trends

Machine Learning Market (By Type: Large Enterprises, Small and Medium Enterprise; By Deployment: Cloud, On-Premises; By End-user: Healthcare, Retail, BFSI, Manufacturing, IT & Telecom, Energy & Utilities, Agriculture, Automotive, Marketing & Advertising) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032

  • Last Updated : July 2023
  • Report Code : 3156
  • Category : ICT

Machine Learning Market Size and Companies

The global machine learning market size was estimated at USD 38.11 billion in 2022 and it is projected to surpass around USD 771.38 billion by 2032, expanding at a CAGR of 35.09% during the forecast period from 2023 to 2032.

Machine Learning Market Size 2023 To 2032

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Key Takeaways:

  • North America dominated the market with the largest market share of 32% in 2022.
  • Asia Pacific is expected to expand at the fastest CAGR between 2023 and 2032.
  • By Type, the large enterprise segment type dominated the market with the largest market share in 2022.
  • By Type, the small enterprise segment type is expected to witness a significant increase in its market revenue during the predicted timeframe.
  • By Deployment, the cloud segment dominated the market with the largest market share in 2022.
  • By Deployment, the on-premise segment is expected to show steady growth during the forecast period.
  • By End-user, the healthcare segment dominated the market with the largest market share in 2022.
  • By End-user, the retail segment is expected to increase its market share during the forecast period.

Machine Learning Market Overview:

Machine learning is the subpart of the branch of artificial intelligence and computer science. It focuses on algorithms and data for increasing accuracy. Machine learning is the technology that allows the computer to learn from past data. Machine learning uses various methods in algorithms for making information from previous data. The accuracy of the information or data is directly dependent on the historical data and the amount of data which helps to make a better model that predicts accurate future data.

Machine learning would minimize the problem and work process of the organizations. Suppose if organizations are stuck somewhere then they just put the data on a machine, and it will analyze the predict the future data and give insights to that problem. It will enhance the predictability of the organization and increase the growth of the market by adapting machine learning technology.

Growth Factors:

Machine learning is an emerging technology that is growing daily due to its technological advancements. Machine learning works with a large amount of data and analyzed it according to the requirement of the organizations. The vast amount of data cannot be managed by humans so for analyzing and managing that data there is the requirement for a computer system so machine learning is used for it. As organizations require technological solutions for managing complex data and operations, the market for machine learning is expected to be accelerated.

Machine learning algorithms not only work on an enormous amount of data but also analyze, construct the model, explore the data, and automatically forecast the required data. The performance of machine learning depends on the algorithms of the vast amounts of data and is determined by cost functions. With the use of machine learning the cost and time will be managed efficiently.

Machine learning can be useful in many fields and industries like automobiles, electronics and healthcare. In the automobile sector, self-driving cars are the major module that is using machine learning technology. Additionally, cybersecurity and face recognition also used machine learning technology. Machine learning is also used by the social media platform or the e-commerce industry to give search results according to the requirement of the consumer by analyzing their previous search history data. Thus, all these factors are highly contributing to the growth of the machine learning market.

Machine Learning Market Scope

Report Coverage Details
Market Size in 2023 USD 51.48 Billion
Market Size by 2032 USD 771.38 Billion
Growth Rate from 2023 to 2032 CAGR of 35.09%
Largest Market North America
Fastest Growing Market Asia Pacific
Base Year 2022
Forecast Period 2023 To 2032
Segments Covered By Type, By Deployment, and By End-user
Regions Covered North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa


Machine Learning Market Dynamics:

Driver:

Easy and efficient automation in business process

The ease with which tedious tasks that are prone to human error can be completed is one of the most significant effects of utilizing machine learning in organizational operations. By automating decision-making processes, machine learning algorithms are shown to be an excellent tool for human assistants. This effort ensures that developers have enough time to innovate new concepts that they would otherwise be unable to work on because of routine tasks. Chatbots and sentiment analysis are two common uses of automation. The penetration of machine learning has promoted the reduction of human errors and interventions in operations. Thus, the easy and efficient automation in the business process offered by machine learning technology is observed to act as a driver for the market.

Restraint:

Data authenticity concerns

Machine learning algorithms mainly work on the data that is provided to the model; hence the results are also generated according to the data provided to it. The authenticity of the data is the major concern in the adoption of machine learning technology.  Collecting the data from a survey and other mediums may contain irregularities and false information. It will result in inaccuracies of results obtained from machine learning algorithms. The improper or imbalanced amount of data is observed to impact the overall operation of the program. These would create hurdles in the growth of the machine learning market by acting as a restraint for the market.

Opportunity:

Rising demand for the identification of patterns

When managing a large number of datasets, machine learning algorithms are extremely dependable. These datasets can range from straightforward real-world customer feedback to multidimensional data; machine learning algorithms are capable of effectively handling any type of data-related inquiries. These data-related inquiries are not only handled but they are also processed in a far more legible and practical way. These transformed datasets are then used to train new datasets with supervised algorithms to find patterns in the product's usage.

It enables developers to meet customer needs in a far more affordable and useful manner. Even in a dynamic setting, machine learning can handle multidimensional data. The identification of patterns has become important in the e-commerce and retail industry to offer generated suggestions and recommendations for consumers, this element opens a wide set of opportunities for the market to grow while boosting the adoption of machine learning technology for multiple end users.

Type Insights:

The large enterprises segment is expected to expand at a robust pace during the forecast period. The growth of the segment is attributed due to the higher need for data management in large-scale enterprises. Machine learning is the leading term in the race for artificial intelligence. Machine learning is considered another advanced breakthrough in the field of artificial intelligence. Machine learning algorithms are trained to make better and more precise decision-making from historical data.  Many of large enterprises use machine learning for making better decisions in the part of inventory, supply chain, customer satisfaction, etc. Many e-commerce industries are utilizing machine learning algorithms in order to generate better results for the search by consumers.

Machine learning algorithms are capable of managing and analyzing consumer data such as their frequent searches, interests, needs, and location for showing better results to them. As well major social media platforms are using machine learning solutions for offering connective suggestions to users. Thus, these will be contributing to the growth of the segment.

Deployment Insights:

The cloud-based segment is expected to hold the largest share of the market throughout the forecast period. The segment’s growth is attributed to the presence of cloud computing making machine learning easily accessible. The rising deployment of cloud machine learning in numerous organizations is due to the offering of high-end services, and computer storage essential for machine learning training algorithms. Cloud computing in machine learning aims to make operations in a firm or organization cost-effective and flexible.

Various enterprises use cloud computing to support machine learning training algorithms or to support artificial intelligence as a service. Machine learning deployment faces multiple hurdles such as lack of specialist to build and train the machine learning and cost of professionals, infrastructure, and development with the requirement of specialized hardware equipment, cloud computing cut of all the expenses without having the technical burden. Cloud computing minimizes many problems that come with machine learning in organizations.

End-User Insights:

The healthcare segment dominated the market with the highest market share in 2022, the segment is anticipated to emerge as the most attractive segment in the market during the forecast period. The growth of the segment is attributed to the rising integration of machine learning solutions in the healthcare industry along with multiple other automation solutions. Machine learning used in the healthcare industry aims to help medical professionals with effective patient care and the management of medical data. In the healthcare sector, machine learning is used to manage the patient's data, recommend treatments, identify healthcare trends, and more.

In recent technological developments, machine learning has created the latest insights in healthcare by offering personalized healthcare solutions, accuracy of diagnosis, and making novel solutions in drug delivery platforms. The major objective for the adoption of machine learning in the healthcare industry is to improve outcomes and medical insights. Machine learning can help healthcare providers in making quick yet accountable predictions of diseases, it can visualize biomedical data and increase the pace of diagnosis by providing proper disease information by analyzing symptoms and history of patients. Machine learning technology helps in developing new medicines and in innovative drugs for the treatment of medical conditions. As the demand for personalized treatment plans and precise medicine increases, the segment is expected to generate significant revenue.

Regional Insights:

North America dominated the market with the largest market size in 2022. The growth of the region is attributed to the rising technological development across the region. Growing adoption of machine learning technology in several industries, mainly in automobile and healthcare, for better workflow of the industry. Countries like the United States and Canada have witnessed the most advanced development in the industrial sector. Additionally, the overall economic condition of these countries makes them the largest contributors to the market’s growth. The higher presence of large-scale technology companies across the region will be contributing to the growth of the machine learning market across the region in the upcoming years.

In addition, considering the customer involvement in purchasing/buying products or services, the industries in North America have focused on transparency of services to boost consumer satisfaction, this is another major factor to induce the adoption of machine learning technology in the form of artificial intelligence or generative AI.

Machine Learning Market Share, By Region, 2022 (%)

Asia Pacific is expected to witness a significant increase in market share during the forecast period. The growth of the region is owing to an emerging trend in technological advancements across the region. The growing adoption of artificial intelligence in several sectors would be contributing to the substantial demand for machine learning solutions across the region. Rising economies in the nation like India and China will result in rapid and easy adoption of such solutions. Along with this, constant efforts on technological advancements and the presence of potential software solution companies in the region highlight the optimism of the market in Asia Pacific.

Recent Development:

  • In July 2023, a leading online learning and upskilling platform, “Geekster” launched the 360-degree platform in machine learning and data science for fulfilling the demand for skilled professional force in the domain. The program is made for an intensive learning experience with Live Learning Hours of over 500+ with industry experts, 25 real-world projects, and a personalized mentor for the learners.
  • In July 2023, Deci AI Ltd., a Deep learning automation startup, announced the launch of an open-source and free artificial intelligence tool that can manage datasets for the model training process. Deci is a manufacturer of machine learning development platform which is used to make, optimize and deploy artificial intelligence in the cloud on mobile or at-edge devices.
  • In July 2023, a leading provider of cybersecurity, performance management, and DDoS attack protection solutions “NETSCOUT SYSTEMS, INC” announced the launch of its newest version of Arbor Edge Defense (AED) includes new machine learning adaptive DDoS Protection.

Machine Learning Market Companies

  • Google
  • Fair Isaac
  • Baidu
  • Hewlett Packard Enterprise Development
  • Intel
  • International Business Machines
  • Microsoft
  • Sap
  • Sas Institute
  • Amazon Web Services
  • Bigml

Segments Covered in the Report:

By Type

  • Large Enterprises
  • Small and Medium Enterprise

By Deployment

  • Cloud
  • On-Premises

By End-user

  • Healthcare
  • Retail
  • BFSI
  • Manufacturing
  • IT & Telecom
  • Energy & Utilities
  • Agriculture
  • Automotive
  • Marketing & Advertising

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East and Africa

Frequently Asked Questions

The global machine learning market size is expected to increase USD 771.38 billion by 2032 from USD 38.11 billion in 2022.

The global machine learning market will register growth rate of 35.09% between 2023 and 2032.

The major players operating in the machine learning market are Google, Fair Isaac, Baidu, Hewlett Packard Enterprise Development, Intel, International Business Machines, Microsoft, Sap, Sas Institute, Amazon Web Services, Bigml, and Others.

The driving factors of the machine learning market are the easy and efficient automation in business process and increases the profitability of the organizations.

North America region will lead the global machine learning market during the forecast period 2023 to 2032.

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology (Premium Insights)

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 Market 

5.1. COVID-19 Landscape: Machine Learning 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 Market, By Type

8.1. Machine Learning Market, by Type, 2023-2032

8.1.1 Large Enterprises

8.1.1.1. Market Revenue and Forecast (2020-2032)

8.1.2. Small and Medium Enterprise

8.1.2.1. Market Revenue and Forecast (2020-2032)

Chapter 9. Global Machine Learning Market, By Deployment

9.1. Machine Learning Market, by Deployment, 2023-2032

9.1.1. Cloud

9.1.1.1. Market Revenue and Forecast (2020-2032)

9.1.2. On-Premises

9.1.2.1. Market Revenue and Forecast (2020-2032)

Chapter 10. Global Machine Learning Market, By End-user 

10.1. Machine Learning Market, by End-user, 2023-2032

10.1.1. Healthcare

10.1.1.1. Market Revenue and Forecast (2020-2032)

10.1.2. Retail

10.1.2.1. Market Revenue and Forecast (2020-2032)

10.1.3. BFSI

10.1.3.1. Market Revenue and Forecast (2020-2032)

10.1.4. Manufacturing

10.1.4.1. Market Revenue and Forecast (2020-2032)

10.1.5. IT & Telecom

10.1.5.1. Market Revenue and Forecast (2020-2032)

10.1.6. Energy & Utilities

10.1.6.1. Market Revenue and Forecast (2020-2032)

10.1.7. Agriculture

10.1.7.1. Market Revenue and Forecast (2020-2032)

10.1.8. Automotive

10.1.8.1. Market Revenue and Forecast (2020-2032)

10.1.9. Marketing & Advertising

10.1.9.1. Market Revenue and Forecast (2020-2032)

Chapter 11. Global Machine Learning Market, Regional Estimates and Trend Forecast

11.1. North America

11.1.1. Market Revenue and Forecast, by Type (2020-2032)

11.1.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.1.3. Market Revenue and Forecast, by End-user (2020-2032)

11.1.4. U.S.

11.1.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.1.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.1.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.1.5. Rest of North America

11.1.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.1.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.1.5.3. Market Revenue and Forecast, by End-user (2020-2032)

11.2. Europe

11.2.1. Market Revenue and Forecast, by Type (2020-2032)

11.2.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.2.3. Market Revenue and Forecast, by End-user (2020-2032)

11.2.4. UK

11.2.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.2.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.2.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.2.5. Germany

11.2.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.2.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.2.5.3. Market Revenue and Forecast, by End-user (2020-2032)

11.2.6. France

11.2.6.1. Market Revenue and Forecast, by Type (2020-2032)

11.2.6.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.2.6.3. Market Revenue and Forecast, by End-user (2020-2032)

11.2.7. Rest of Europe

11.2.7.1. Market Revenue and Forecast, by Type (2020-2032)

11.2.7.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.2.7.3. Market Revenue and Forecast, by End-user (2020-2032)

11.3. APAC

11.3.1. Market Revenue and Forecast, by Type (2020-2032)

11.3.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.3.3. Market Revenue and Forecast, by End-user (2020-2032)

11.3.4. India

11.3.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.3.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.3.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.3.5. China

11.3.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.3.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.3.5.3. Market Revenue and Forecast, by End-user (2020-2032)

11.3.6. Japan

11.3.6.1. Market Revenue and Forecast, by Type (2020-2032)

11.3.6.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.3.6.3. Market Revenue and Forecast, by End-user (2020-2032)

11.3.7. Rest of APAC

11.3.7.1. Market Revenue and Forecast, by Type (2020-2032)

11.3.7.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.3.7.3. Market Revenue and Forecast, by End-user (2020-2032)

11.4. MEA

11.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.4.4. GCC

11.4.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.4.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.4.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.4.5. North Africa

11.4.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.4.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.4.5.3. Market Revenue and Forecast, by End-user (2020-2032)

11.4.6. South Africa

11.4.6.1. Market Revenue and Forecast, by Type (2020-2032)

11.4.6.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.4.6.3. Market Revenue and Forecast, by End-user (2020-2032)

11.4.7. Rest of MEA

11.4.7.1. Market Revenue and Forecast, by Type (2020-2032)

11.4.7.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.4.7.3. Market Revenue and Forecast, by End-user (2020-2032)

11.5. Latin America

11.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.5.3. Market Revenue and Forecast, by End-user (2020-2032)

11.5.4. Brazil

11.5.4.1. Market Revenue and Forecast, by Type (2020-2032)

11.5.4.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.5.4.3. Market Revenue and Forecast, by End-user (2020-2032)

11.5.5. Rest of LATAM

11.5.5.1. Market Revenue and Forecast, by Type (2020-2032)

11.5.5.2. Market Revenue and Forecast, by Deployment (2020-2032)

11.5.5.3. Market Revenue and Forecast, by End-user (2020-2032)

Chapter 12. Company Profiles

12.1. Google

12.1.1. Company Overview

12.1.2. Product Offerings

12.1.3. Financial Performance

12.1.4. Recent Initiatives

12.2. Fair Isaac

12.2.1. Company Overview

12.2.2. Product Offerings

12.2.3. Financial Performance

12.2.4. Recent Initiatives

12.3. Baidu

12.3.1. Company Overview

12.3.2. Product Offerings

12.3.3. Financial Performance

12.3.4. Recent Initiatives

12.4. Hewlett Packard Enterprise Development

12.4.1. Company Overview

12.4.2. Product Offerings

12.4.3. Financial Performance

12.4.4. Recent Initiatives

12.5. Intel

12.5.1. Company Overview

12.5.2. Product Offerings

12.5.3. Financial Performance

12.5.4. Recent Initiatives

12.6. International Business Machines

12.6.1. Company Overview

12.6.2. Product Offerings

12.6.3. Financial Performance

12.6.4. Recent Initiatives

12.7. Microsoft

12.7.1. Company Overview

12.7.2. Product Offerings

12.7.3. Financial Performance

12.7.4. Recent Initiatives

12.8. Sap

12.8.1. Company Overview

12.8.2. Product Offerings

12.8.3. Financial Performance

12.8.4. Recent Initiatives

12.9. Sas Institute

12.9.1. Company Overview

12.9.2. Product Offerings

12.9.3. Financial Performance

12.9.4. Recent Initiatives

12.10. Amazon Web Services

12.10.1. Company Overview

12.10.2. Product Offerings

12.10.3. Financial Performance

12.10.4. Recent Initiatives

Chapter 13. Research Methodology

13.1. Primary Research

13.2. Secondary Research

13.3. Assumptions

Chapter 14. Appendix

14.1. About Us

14.2. Glossary of Terms

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