Machine Learning in Communication Market by Deployment Type (Cloud-Based, On-Premise), Organization Size, Deployment, Application (Network Optimization, Predictive Maintenance, Virtual Assistants, Robotic Process Automation (RPA)), and Region (North America, Europe, Asia Pacific, Middle East, and Africa, South America), Global Forecast 2018 to 2028
Aug 2020
Total Pages: 127
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The global machine learning in communication market size is anticipated to reach USD 4.2 billion by 2028. Adoption of intelligent communication designs, remarkable growth in data traffic, and advancements in technologies are some of the key reasons responsible for the industry growth are a few of the key factors anticipated to boost the industry growth in the coming years. Moreover, developments in technologies such as the Internet of Things (IoT), and big data are also fuelling the demand for machine learning in communication market growth.
Huge volumes of network data crossing networks on a routine basis. Machine language provides enhanced flexibility and know-how in network engineering, operation, and management, thus maximizing network control and efficiency, thereby increasing the ultimate application of the computer language in communications and telecommunications. This development is due to machine learning as a crucial method for evaluating network and controlling traffic automation. With the advent of large-data analytics, network-rich metrics have been used to provide unprecedented analysis of The infrastructure.
Key Segments of the Global Machine Learning in Communication Market
Deployment Type Overview, 2019-2028 (USD Million)
- On-Premise
- Cloud-Based
Application Overview, 2019-2028 (USD Million)
- Predictive Maintenance
- Network Optimization
- Robotic Process Automation (RPA)
- Virtual Assistants
- Others
Regional Overview, 2019-2028 (USD Million)
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- Rest of Asia-Pacific
- Middle East and Africa
- UAE
- South Africa
- Rest of Middle East and Africa
- South America
- Brazil
- Rest of South America
Reasons for the study
- The purpose of the study is to give an exhaustive outlook of the global machine learning in the communication market. Benchmark yourself against the rest of the market.
- Ensure you remain competitive as innovations by existing key players to boost the market.
What does the report include?
- The study on the global machine learning in communication market includes qualitative factors such as drivers, restraints, and opportunities
- The study covers the competitive landscape of existing/prospective players in the machine learning in the communication industry and their strategic initiatives for the product development
- The study covers a qualitative and quantitative analysis of the market segmented based on Deployment Type, organization size, deployment, and application. Moreover, the study provides similar information for the key geographies.
- Actual market sizes and forecasts have been provided for all the above-mentioned segments.
Who should buy this report?
- This study is suitable for industry participants and stakeholders in the global machine learning in the communication market. The report will benefit:
- Every stakeholder is involved in machine learning in the communication market.
- Managers within the Tech companies looking to publish recent and forecasted statistics about global machine learning in the communication market.
- Government organizations, regulatory authorities, policymakers, and organizations looking for investments in trends of global machine learning in the communication market.
- Analysts, researchers, educators, strategy managers, and academic institutions looking for insights into the market to determine future strategies.
Moreover, machine learning methods are a core part of many emerging applications of communication technology. Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities. Moreover, a learned communication system allows wireless designers to prioritize key design parameters such as throughput, latency, range, and power consumption.
Residential healthcare systems usually employ wearable devices, camera-based vision systems, and ambient sensors, but they entail drawbacks such as physical discomfort, privacy concerns, and limited detection accuracy. On the other hand, a passive Wi-Fi sensing system, based on activity recognition and through-wall respiration sensing, is contactless, accurate, and minimally invasive.
Deployment Type Segment
The global machine learning in the communication market is classified into cloud-based and on-premise. The cloud-based segment accumulated the largest market share in machine learning in the communication market in 2019. However, the on-premise segment is anticipated to witness a substantial growth rate over the forecast period.
Application Segment
Based on the application, the market is segmented into predictive maintenance, network optimization, robotic process automation (RPA), virtual assistants, and others. The market for robotic process automation (RPA) is anticipated to possess the largest market share in 2019 since the Tech companies today are predominantly developing the automation to match their user needs. Moreover, the growing regulatory scrutiny coupled with enhanced customer satisfaction, as well as advantages such as intelligent network management to improve situational awareness, improving network self-optimization and performance are some of the factors responsible for the machine learning in communication demand.
The global Machine Learning in Communication market is a wide range to North America, Europe, APAC, South America, and the Middle East & Africa. North America is considered a mature market in machine learning in communication applications, owing to the outsized presence of an organization with the availability of technical expertise and advanced IT infrastructure. The US and Canada are the highest contributory countries to the expansion of machine learning in the communication market in North America.
The major players of the global machine learning in the communication market are IBM, Cisco Nexmo, Google, Dialpad, Nextiva, Amazon, Microsoft, Twilio, RingCentral, and others. Machine learning in the communication market is fragmented with the existence of well-known global and domestic players across the globe.
The global machine learning in communication market size is anticipated to reach USD 4.2 billion by 2028. Adoption of intelligent communication designs, remarkable growth in data traffic, and advancements in technologies are some of the key reasons responsible for the industry growth are a few of the key factors anticipated to boost the industry growth in the coming years. Moreover, developments in technologies such as the Internet of Things (IoT), and big data are also fuelling the demand for machine learning in communication market growth.
Huge volumes of network data crossing networks on a routine basis. Machine language provides enhanced flexibility and know-how in network engineering, operation, and management, thus maximizing network control and efficiency, thereby increasing the ultimate application of the computer language in communications and telecommunications. This development is due to machine learning as a crucial method for evaluating network and controlling traffic automation. With the advent of large-data analytics, network-rich metrics have been used to provide unprecedented analysis of The infrastructure.
Key Segments of the Global Machine Learning in Communication Market
Deployment Type Overview, 2019-2028 (USD Million)
- On-Premise
- Cloud-Based
Application Overview, 2019-2028 (USD Million)
- Predictive Maintenance
- Network Optimization
- Robotic Process Automation (RPA)
- Virtual Assistants
- Others
Regional Overview, 2019-2028 (USD Million)
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- Rest of Asia-Pacific
- Middle East and Africa
- UAE
- South Africa
- Rest of Middle East and Africa
- South America
- Brazil
- Rest of South America
Reasons for the study
- The purpose of the study is to give an exhaustive outlook of the global machine learning in the communication market. Benchmark yourself against the rest of the market.
- Ensure you remain competitive as innovations by existing key players to boost the market.
What does the report include?
- The study on the global machine learning in communication market includes qualitative factors such as drivers, restraints, and opportunities
- The study covers the competitive landscape of existing/prospective players in the machine learning in the communication industry and their strategic initiatives for the product development
- The study covers a qualitative and quantitative analysis of the market segmented based on Deployment Type, organization size, deployment, and application. Moreover, the study provides similar information for the key geographies.
- Actual market sizes and forecasts have been provided for all the above-mentioned segments.
Who should buy this report?
- This study is suitable for industry participants and stakeholders in the global machine learning in the communication market. The report will benefit:
- Every stakeholder is involved in machine learning in the communication market.
- Managers within the Tech companies looking to publish recent and forecasted statistics about global machine learning in the communication market.
- Government organizations, regulatory authorities, policymakers, and organizations looking for investments in trends of global machine learning in the communication market.
- Analysts, researchers, educators, strategy managers, and academic institutions looking for insights into the market to determine future strategies.
Chapter 1 Introduction
1.1 Introduction to the Study
1.2 Market Definition and Scope
1.3 Units, Currency, Conversions and Years Considered
1.4 Key Stakeholders
1.5 Key Questions Answered
Chapter 2 Research Methodology
2.1 Introduction
2.2 Data Capture Sources
2.2.1 Primary Sources
2.2.2 Secondary Sources
2.3 Market Size Estimation
2.4 Market Forecast
2.5 Data Triangulation
2.6 Assumptions and Limitations
Chapter 3 Executive Summary
Chapter 4 Market Outlook
4.1 Introduction
4.2 Value Chain Analysis
4.3 Market Dynamics
4.3.1 Drivers
4.3.2 Restraints
4.3.3 Opportunities
4.5 PEST Analysis
Chapter 5 Machine Learning in Communication market by Deployment Type
5.1 Introduction
5.1.1 Cloud-based
5.1.2 On-premise
Chapter 6 Machine Learning in Communication market by Application
6.1 Introduction
6.1.1 Predictive Maintenance
6.1.2 Network Optimization
6.1.3 Robotic Process Automation (RPA)
6.1.4 Virtual Assistants
6.1.5 Others
Chapter 7 Machine Learning in Communication market By Region
7.1 Introduction
7.2 North America
7.2.1 U.S.
7.2.2 Canada
7.3 Europe
7.3.1 Germany
7.3.2 France
7.3.3 UK
7.3.4 Rest of Europe
7.4 Asia Pacific
7.4.1 China
7.4.2 Japan
7.4.3 India
7.4.4 Rest of Asia Pacific
7.5 Middle East & Africa
7.5.1 UAE
7.5.2 South Africa
7.5.3 Rest of Middle East & Africa
7.6 South America
7.6.1 Brazil
7.6.2 Rest of South America
Chapter 8 Competitive Landscape
8.1 Overview
8.2 Strategic Initiatives
8.2.1 Mergers & Acquisitions
8.2.2 New Product Launch
8.2.3 Investments
8.2.4 Expansion
8.2.5 Others
Chapter 9 Company Profiles
9.1 Google
9.1.1 Overview
9.1.2 Products and Services Portfolio
9.1.3 Recent Initiatives
9.1.4 Company Financials
9.1.5 SWOT
9.2 Microsoft Corporation
9.2.1 Overview
9.2.2 Products and Services Portfolio
9.2.3 Recent Initiatives
9.2.4 Company Financials
9.2.5 SWOT
9.3 IBM Corporation
9.3.1 Overview
9.3.2 Products and Services Portfolio
9.3.3 Recent Initiatives
9.3.4 Company Financials
9.3.5 SWOT
9.4 Cisco
9.4.1 Overview
9.4.2 Products and Services Portfolio
9.4.3 Recent Initiatives
9.4.4 Company Financials
9.4.5 SWOT
9.5 Dialpad
9.5.1 Overview
9.5.2 Products and Services Portfolio
9.5.3 Recent Initiatives
9.5.4 Company Financials
9.5.5 SWOT
9.6 Nexmo
9.6.1 Overview
9.6.2 Products and Services Portfolio
9.6.3 Recent Initiatives
9.6.4 Company Financials
9.6.5 SWOT
9.7 Nextiva
9.7.1 Overview
9.7.2 Products and Services Portfolio
9.7.3 Recent Initiatives
9.7.4 Company Financials
9.7.5 SWOT
9.8 Amazon
9.8.1 Overview
9.8.2 Products and Services Portfolio
9.8.3 Recent Initiatives
9.8.4 Company Financials
9.8.5 SWOT
9.9 Twilio
9.9.1 Overview
9.9.2 Products and Services Portfolio
9.9.3 Recent Initiatives
9.9.4 Company Financials
9.9.5 SWOT
9.10 RingCentral
9.10.1 Overview
9.10.2 Products and Services Portfolio
9.10.3 Recent Initiatives
9.10.4 Company Financials
9.10.5 SWOT