Machine Learning Market To Reach USD 35.67 Billion By 2026 | Reports And Data

Thu Jun 25, 2020 - 2:15am GMT+0000

The increasing trend towards cloud computing, coupled with the growing technological advancements such as BYOD services, the internet, and others, will contribute to the growth of the market.

New York City, United States – June 24, 2020 /MarketersMedia/

The global machine learning market is forecast to reach USD 35.67 Billion by 2026, according to a new report by Reports and Data. The increasing trend towards cloud computing, coupled with the growing technological advancements such as BYOD services, the internet, and others, will contribute to the growth of the market. Machine learning helps in analyzing large volumes of data and identify specific trends and patterns that would not be apparent otherwise. For instance, for an e-commerce website such as Amazon, it helps to understand the purchase histories and browsing behaviors of its users to help cater to the right opportunities, products, and reminders relevant to them.

This is the latest report covering the current COVID-19 scenario. The coronavirus pandemic has greatly affected every aspect of the worldwide industry. It has brought along various changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact are covered in the research report. The report discusses all the major aspects of the market with expert opinions on the current status along with historical data.

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Key players mentioned in the research report are:
IBM Corporation, Microsoft Corporation, SAP SE, Dell Inc., SAS Institute Inc., Google, Inc., Amazon Web Services Inc., Baidu, Inc., BigML, Inc., Intel Corporation, RapidMiner, Inc., Hewlett Packard Enterprise (HPE), Angoss Software Corporation, Alpine Data, Dataiku, Luminoso Technologies, Inc., TrademarkVision, Fractal Analytics Inc., TIBCO Software Inc., Teradata, and Oracle Corporation, among others.

In terms of applications, the global Machine Learning market can be segmented into:
Fraud Detection & Risk Analytics
Augmented & Virtual reality
Natural Language processing
Computer vision
Security & surveillance
Marketing & Advertisement
Automated Network Management
Predictive Maintenance
Others

In terms of types, the global Machine Learning market can be segmented into:
Automotive
Aerospace & Defense
Retail & E-commerce
Government
Healthcare And Life Sciences
Media And Entertainment
IT And Telecommunications
Banking, Financial Services, And Insurance
Others

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For geographical segmentation, regional supply, application-wise, and type-wise demand, key players, and others, this report covers the following regions: North America, Europe, Asia-Pacific, South America, and Middle East & Africa. The report sheds light on the competitive landscape of the market that covers the product offerings, services, market shares, and business overview. This Machine Learning Market research report covers various dynamic aspects like the market drivers, restraints and challenges, and growth prospects. The prominent and leading companies are profiled in the report.

Further key findings from the report suggest

• Among the applications, fraud detection & risk analytics accounted for the largest market share of ~18% in the year 2018. Fraud detection & risk analytics has been a significant concern in the banking and commerce industry. The increased number of transactions owing to a plethora of payment channels – smartphones, credit/debit cards, and others. As a result, it is becoming difficult for businesses to authenticate their transactions.

• Machines are much better at processing large datasets than humans. They can detect and recognize thousands of patterns on a user’s purchasing course. The fraud detection process using machine learning starts with gathering and classifying the data. Then the model is fed with training sets to predict the probability of fraud.

• The predictive maintenance application is forecasted to witness the highest growth rate of 44.4% during the forecast period. Manufacturing firms need corrective as well as preventive maintenance practices in place. However, these practices are often inefficient and expensive. Machine learning helps in the creation of highly efficient predictive maintenance programs. These can minimize the chances of unexpected failures, thereby reducing unnecessary preventive maintenance projects.

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