The growing need for automation in networking efficiency with network self-optimization drives the demand for machine learning in the communication market 

The machine learning in communication market growth prospects has been showing great promise all over the world with immense growth potential in terms of revenue generation and this growth is expected to be huge by 2028. 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.

Machine learning is a computing technology that allows computers to learn and change their analysis functionalities without directly being programmed when introduced to new data sets. In recent years the worldwide data traffic has risen explosively and is projected to steadily lower the efficiency of future connectivity networks in the age of the latest generation of communication systems. Moreover, in addition to a significant increase in data traffic, the emergence of new communications applications, such as wearable appliances, stand-alone systems, drones, and the Internet of Things (IoT), continues to generate even more data transmission with very different performance requirements.

This development in the technology field is continuously increasing the need to make communication networks more intelligent to manage, run, and automate. Modern machine learning techniques enable sophisticated communications designs to solve a variety of problems from signal identification, classification, and fragmented signal retrieval to channel simulation, network optimization, capacity management, routing, the design of transport protocols, and study of applications/user behavior. Machine learning algorithms also play an increasingly significant role in fields such as digital networking.

The industry growth is influenced by various factors such as industrial manufacturing activity following the current market situation and demand, which in some period seems to be exhibiting substantial upward trends due to acquisitions, emerging technologies, assessment of, and introduction of new technology. The advancement of embedded AI is expected to allow newly available knowledge to help ML algorithms to learn continuously. In the coming years, these technologies will drive the market. Machine language offers more functionality and expertise in network architecture, service, and maintenance, while improving network self-optimization and performance, therefore the overall adoption of the machine language is increasing in the communication and telecommunication fields. Beside intelligent network management to improve situational awareness and network operations, machine learning would allow future networking networks and their implementations, e.g. IoT, to take advantage of Big Data Analytics, thereby increasing the demand for machine learning in communication and expected to grow significantly throughout the forecast period. Cities of the future shall draw on the intelligent use of knowledge with AI and machine learning offering data-driven insight to enable cyber-physical systems to autonomously change their actions for performance.

The ICT industry is supposed to help solve the problems raised by 5G and the Internet of Things, improvements that represent major rises in network sophistication, and the variety of system demands. however, several problems and concerns need to be resolved given the active use of machine learning systems in many communications applications. For instance, the large size and computation specifications of contemporary machine learning algorithms preclude the wide use of these models in embedded devices. Besides, 5G networks need new machine-based solutions to controlling radio infrastructure and maintaining networks that can deal with the complexities and the inconsistent knowledge of channels and network states.

The report includes a detailed description of the current COVID-19 impact on global machine learning in the communication market. The research also shines a light on different facets of low code development applications in the communications sector by using value chain analysis to analyze the market. The study discusses many qualitative facets of the low-code platform industry in market dynamics, market constraints, and key developments in the sector. The study includes an in-depth market rivalry evaluation with company profiles of both multinational and local vendors.

The geographically global machine learning in the communication market is segmented into North America, Europe, APAC, South America, and the Middle East & Africa. Machine Learning Solutions is North America's most forward-looking. Besides, this area is highly receptive to advances in technology such as cloud computing technology, Big Data, and machine learning systems. For many systems and fields, North America is the primary provider of computer learning resources. The United States and Canada are the major contributors to the growth of the North American cloud apps market.

The major players of the global machine learning in the Communication market include 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.

Segment Overview of Global Machine Learning in Communication Market

Deployment Type Overview, 2019-2028 (USD Billion)

• On-Premise
• Cloud-Based

Application Overview, 2019-2028 (USD Billion)

• Predictive Maintenance
• Network Optimization
• Robotic Process Automation (RPA)
• Virtual Assistants
• Others

Regional Overview, 2018-2025 (USD Billion)

• 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