Dublin, Sept. 03, 2021 (GLOBE NEWSWIRE) — The “Machine Learning Market Size, Market Share, Application Analysis, Regional Outlook, Growth Trends, Key Players, Competitive Strategies and Forecasts, 2021 To 2029” report has been added to’s offering.

The global machine learning market is estimated to grow at a CAGR of 43.3% during the forecast period (2021-2029). The major drivers for machine learning market are proliferation in data generation, technological advancements in machine learning, increasing adoption of connected devices and increased adoption in data driven application. Enterprises are awash in data related to their customers, prospects, internal business processes, suppliers, partners and competitors. Often, they can’t control this flood of data and convert it to actionable information for growing revenue, increasing profitability and efficiently operating the business. Organizations of all disciplines across the globe suffer a serious problem of managing data in the form of data retention, understanding dark data, data integration for proper analytics, data access and others.

Volume of Data Collected Now and in Future to Raise Exponentially

Machine learning offers a promising solution to gain economic benefits from the increasing data with the help of predictive analysis and reducing fraud. The volume of data collected by business worldwide is estimated to double every year and lack of understanding of data is cited as a primary reason that overruns project cost, and that may cost business approximately 20% to 35% of their operating revenue. Big data capabilities assist in providing constant changing customers preferences that help companies by assisting them by improving customer’s satisfaction, faster decision making, developing strategies for launching new products and exploring new market.

ML in BFSI Remains Indispensable:

Global machine learning market has been segmented on the basis of verticals, deployment modes, organization size and service. The vertical segment is further sub segmented into banking, financial services, and insurance, retail, telecommunication, healthcare and life sciences, manufacturing, government and defense, energy and utilities and other verticals. BFSI segment leads the vertical segment in terms of revenue in the global machine learning market with around 21.9% market share in 2020. The BSFI segment is primarily driven by growing demand of ML in the BFSI sector to automate the process of loan approval, for fraud prevention, risk management, investment predictions, marketing and others. Prominent banks across the globe including JPMorgan Chase, Wells Fargo, Bank of America, Citibank, U.S. Bank and others have adopted the machine learning to realize the potential benefits of data driven decision.

ML in Healthcare Application Promising Significant Opportunities

The healthcare and life science vertical is anticipated to grow at the highest rate over the forecast period growing at a CAGR of 44.3% over the forecast period. The high growth rate is attributed to the fact that ML solution has wide potentials across healthcare industry. These include patient data & risk analysis, in patient care & hospital management, medical imaging & diagnosis, drug discovery, life style monitoring & management, medical diagnosis & imaging, precision medicine and others. Furthermore, key companies are providing various machine learning systems across healthcare that includes Google Deep Mind Health, IBM Watson and others. Moreover, increasing healthcare expenditure also leverages huge adoption opportunities for the machine learning. According to the Institute of Health Metrics and Evaluations, global healthcare expenditure is expected to reach $18.28 trillion globally by 2040.

NA Leads Revenues APAC to Lead the Growth:

Geographically, global machine learning market has been segmented into North America, Europe, Asia Pacific and Rest of the world (ROW). North America leads the global machine learning market by capturing largest market share in terms of revenue of 36.96% in 2020. The U.S. market for machine learning is primarily driven high adoption of machine learning solutions by both public and private organization, coupled with technological sound infrastructure and proactive government support to artificial intelligence. Furthermore, public as well as private sector are embracing machine learning to realize the benefits of data driven decision which is expected to create lucrative growth opportunities for the machine learning market in North America.

For instance, several companies in the North America region including Walmart, Facebook, Amazon, General Motors, Tesla and others have included machine learning as a part of their marketing strategy, as well as are focusing on deployment of ML to realize operational efficiency within the supply chain. Asia Pacific machine learning system market is anticipated to grow at highest CAGR of 44.1 % during the forecast period. Emergence of machine learning startups, growth in the BFSI, manufacturing and healthcare sector coupled with growing adoption of artificial intelligence by both the private and public sectors along with the investments in artificial intelligence by the governments of countries such as India, China, Japan, etc are the major factors augmenting growth in the machine learning market in Asia Pacific. For instance, China government is focusing on the development of artificial intelligence and machine learning in innovating the abilities of robotics, inventory forecasting and developing driverless car technology in the country.

M&A Remains as Key Strategy to Enhance Market Share:

Merger & acquisition is the primary strategy adopted by companies in global machine learning market. Merger & acquisition is enabling the companies to enhance market through effective leverage of product portfolio and global reach. Product development is the secondary strategy adopted by companies in the global machine learning market. Key market players in this segment include IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US) and others.

Key questions answered in this report

  • What are the key market segments in current scenario and in the future by product categories?
  • What are the key market segments in current scenario and in the future by regions?
  • What is the key impact of Covid-19 over market revenues and market determinants in the global machine learning market?
  • What are the primary and secondary macro and micro factors influencing the market growth currently and during the forecast period?
  • What are the primary and secondary macro and micro factors deterring the market growth currently and during the forecast period?
  • How to overcome the current market challenges and leverage the opportunities in each of the market segment?
  • Who are the key players in the operational predictive maintenance market and what are their key product categories and strategies?
  • What are the key strategies – mergers/acquisitions/R&D/strategic partnerships etc that companies are deploying to enhance market revenues and growth?

Key Topics Covered:

Chapter 1. Preface

Chapter 2. Executive Summary

Chapter 3. Market Determinants

3.1. Market Drivers

3.1.1. Proliferation in Data Generation

3.1.2. Technological Advancements in Machine Learning

3.1.3. Increasing Adoption of Connected Devices

3.1.4. Increased Adoption in Data Driven Application

3.2. Market Restraint

3.2.1. Sensitive Data Security

3.2.2. Computation Limitations

3.3. Market Opportunity

3.3.1. Increasing Demand for intelligent Business Processes

3.3.2. High Demand From Different End Users

3.4. Market Challenge

3.4.1. Ethical Implications of Algorithms Deployed

3.4.2. Prone To Hardware and Software Malfunction

Chapter 4. Global Machine Learning Market by Application/Vertical 2019-2029 ($ Million)

4.1. Global Banking, Financial Services, and insurance Market 2019-2029 ($ Million)

4.2. Global Healthcare and Life Sciences Market 2019-2029 ($ Million)

4.3. Global Retail Market 2019-2029 ($ Million)

4.4. Global Telecommunication Market 2019-2029 ($ Million)

4.5. Global Government and Defense Market 2019-2029 ($ Million)

4.6. Global Manufacturing Market 2019-2029 ($ Million)

4.7. Global Energy and Utilities Market 2019-2029 ($ Million)

4.8. Global Other Verticals Market 2019-2029 ($ Million)

Chapter 5. Global Machine Learning Market by Deployment Mode 2019-2029 ($ Million)

5.1. Global Cloud Market 2019-2029 ($ Million)

5.2. Global On-Premises Market 2019-2029 ($ Million)

Chapter 6. Global Machine Learning Market by Organization Size 2019-2029 ($ Million)

6.1. Global Large Enterprises Market 2019-2029 ($ Million)

6.2. Global Small and Medium-Sized Enterprises Market 2019-2029 ($ Million)

Chapter 7. Global Machine Learning Market by Services 2019-2029 ($ Million)

7.1. Global Professional Services Market 2019-2029 ($ Million)

7.2. Global Managed Services Market 2019-2029 ($ Million)

Chapter 8. North America Machine Learning Market 2019-2029 ($ Million)

Chapter 9. Europe Machine Learning Market 2019-2029 ($ Million)

Chapter 10. Asia-Pacific Machine Learning Market 2019-2029 ($ Million)

Chapter 11. Rest of World Machine Learning Market 2019-2029 ($ Million)

Chapter 12. Company Profile

12.1. Amazon Web Services inc.

12.1.1. Overview

12.1.2. Product Portfolio

12.1.3. Strategic initiatives

12.1.4. SCOT Analysis

12.1.5. Strategic Analysis

12.2. Baidu inc.

12.2.1. Overview

12.2.2. Product Portfolio

12.2.3. SCOT Analysis

12.2.4. Strategic Analysis

12.3. Dell, inc.

12.3.1. Overview

12.3.2. Product Portfolio

12.3.3. Strategic initiatives

12.3.4. SCOT Analysis

12.3.5. Strategic Analysis

12.4. Fair Issac Corporation (Fico)

12.4.1. Overview

12.4.2. Product Portfolio

12.4.3. Strategic Initiatives

12.4.4. SCOT Analysis

12.4.5. Strategic Analysis

12.5. Fractal Analytics

12.5.1. Overview

12.5.2. Product Portfolio

12.5.3. Strategic initiatives

12.5.4. SCOT Analysis

12.5.5. Strategic Analysis

12.6. Google

12.6.1. Overview

12.6.2. Product Portfolio

12.6.3. Strategic initiatives

12.6.4. SCOT Analysis

12.6.5. Strategic Analysis

12.7. Hewlett Packard Enterprise (Hpe)

12.7.1. Overview

12.7.2. Product Portfolio

12.7.3. SCOT Analysis

12.7.4. Strategic Analysis

12.8. Ibm Corporation

12.8.1. Overview

12.8.2. Product Portfolio

12.8.3. Strategic initiatives

12.8.4. SCOT Analysis

12.8.5. Strategic Analysis

12.9. intel Corporation

12.9.1. Overview

12.9.2. Product Portfolio

12.9.3. Strategic initiatives

12.9.4. SCOT Analysis

12.9.5. Strategic Analysis

12.10. Microsoft Corporation

12.10.1. Overview

12.10.2. Product Portfolio

12.10.3. Strategic initiative

12.10.4. SCOT Analysis

12.10.5. Strategic Analysis

12.11. Oracle Corporation

12.11.1. Overview

12.11.2. Product Portfolio

12.11.3. SCOT Analysis

12.11.4. Strategic Analysis

12.12. Sap Se

12.12.1. Overview

12.12.2. Product Portfolio

12.12.3. Strategic initiatives

12.12.4. SCOT Analysis

12.12.5. Strategic Analysis

12.13. The information Bus Company (Tibco) Software inc.

12.13.1. Overview

12.13.2. Product Portfolio

12.13.3. Strategic initiative

12.13.4. SCOT Analysis

12.13.5. Strategic Analysis

12.14. Trademarkvision

12.14.1. Overview

12.14.2. Product Portfolio

12.14.3. Strategic initiatives

12.14.4. SCOT Analysis

12.14.5. Strategic Analysis

12.15. Teradata Corporation

12.15.1. Overview

12.15.2. Product Portfolio

12.15.3. SCOT Analysis

12.15.4. Strategic Analysis

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