SON (Self-Organizing Network) technology minimizes the lifecycle cost of running a mobile network by eliminating manual configuration of network elements at the time of deployment right through to dynamic optimization and troubleshooting during operation. Besides improving network performance and customer experience, SON can significantly reduce the cost of mobile operator services, improving the OpEx-to-revenue ratio and deferring avoidable CapEx.

Early adopters of SON have already witnessed a multitude of benefits in the form of accelerated 5G NR and LTE RAN (Radio Access Network) rollout times, simplified network upgrades, fewer dropped calls, improved call setup success rates, higher end user throughput, alleviation of congestion during special events, increased subscriber satisfaction and loyalty, operational efficiencies such as energy and cost savings, and freeing up radio engineers from repetitive manual tasks.

Although SON was originally developed as an operational approach to streamline and automate cellular RAN deployment and optimization, mobile operators and vendors are increasingly focusing on integrating new capabilities such as self-protection against digital security threats and self-learning through AI (Artificial Intelligence) techniques, as well as extending the scope of SON beyond the RAN to include both mobile core and transport network segments – which will be critical to address 5G requirements such as end-to-end network slicing.

In addition, with the cellular industry’s ongoing shift towards open interfaces, virtualization and software-driven networking, the SON ecosystem is progressively transitioning from the traditional D-SON (Distributed SON) and C-SON (Centralized SON) approach to open standards-based components supporting RAN programmability for advanced automation and intelligent control.

The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs.

This report estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025. Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period.

The “SON (Self-Organizing Networks) in the 5G & Open RAN Era: 2022 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents a detailed assessment of the SON market, including the value chain, market drivers, barriers to uptake, enabling technologies, functional areas, use cases, key trends, future roadmap, standardization, case studies, ecosystem player profiles and strategies. The report also provides global and regional market size forecasts for both SON and conventional mobile network optimization from 2022 till 2030, including submarket projections for three network segments, six SON architecture categories, four access technologies and five regional submarkets.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Topics Covered

The report covers the following topics:
– Introduction to SON
– Value chain and ecosystem structure
– Market drivers and challenges
– SON technology, architecture and functional areas
– D-SON (Distributed SON), C-SON (Centralized SON), H-SON (Hybrid SON), RIC (RAN Intelligent Controller), xApps and rApps
– Review of over 40 SON use cases across the RAN, core and transport domains, ranging from ANR (Automatic Neighbor Relations) and rapid equipment configuration to advanced traffic steering, QoE-based optimization and automated anomaly detection
– Key trends in next-generation 5G SON implementations, including Open RAN and vRAN (Virtualized RAN) architectures, dynamic spectrum management, network slicing, edge computing, Big Data, advanced analytics, AI (Artificial Intelligence)/ML (Machine Learning) and zero-touch automation
– Case studies of 20 commercial-scale SON deployments and examination of ongoing projects covering both traditional D-SON/C-SON and RIC-x/rApp approaches
– Future roadmap for the SON market
– Standardization, regulatory and collaborative initiatives
– Profiles and strategies of more than 230 ecosystem players
– Strategic recommendations for SON solution providers and mobile operators
– Market analysis and forecasts from 2022 till 2030

Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

SON & Mobile Network Optimization
– SON
– Conventional Mobile Network Planning & Optimization

SON Network Segment Submarkets
– RAN (Radio Access Network)
– Mobile Core
– Transport (Fronthaul, Midhaul & Backhaul)

RAN Segment SON Architecture Submarkets
– Traditional D-SON & C-SON
○ Embedded D-SON (Distributed SON) Features
○ Third Party C-SON (Centralized SON) & OSS Platforms
– Open RAN RIC, xApps & rApps
○ RIC (RAN Intelligent Controller) Platforms
○ Near Real-Time xApps
○ Non Real-Time rApps
– Mobile Operators’ In-House SON Tools & Systems

SON Access Network Technology Submarkets
– 2G & 3G
– LTE
– 5G NR
– Wi-Fi & Others

Regional Markets
– North America
– Asia Pacific
– Europe
– Middle East & Africa
– Latin & Central America

Key Questions Answered

The report provides answers to the following key questions:
– How big is the SON opportunity?
– What trends, drivers and challenges are influencing its growth?
– What will the market size be in 2025, and at what rate will it grow?
– Which submarkets and regions will see the highest percentage of growth?
– How do SON investments compare with spending on conventional mobile network optimization?
– What are the practical, quantifiable benefits of SON – based on live, commercial deployments?
– How can mobile operators capitalize on SON to ensure optimal network performance, improve customer experience, reduce costs, and drive revenue growth?
– What is the status of D-SON and C-SON adoption worldwide?
– When will open standards-based RIC platforms, xApps and rApps replace the traditional SON approach?
– What are the prospects of AI/ML-driven automation in the SON market?
– What opportunities exist for SON capabilities in the mobile core and transport network domains?
– How can SON ease the deployment of private 4G/5G networks for enterprises and vertical industries?
– In what way will SON facilitate network slicing and other advanced 5G capabilities?
– How does SON impact mobile network optimization engineers?
– Who are the key ecosystem players, and what are their strategies?
– What strategies should SON solution providers and mobile operators adopt to remain competitive?

Key Findings

The report has the following key findings:
– The surging popularity of innovative Open RAN and vRAN (Virtualized RAN) architectures has reignited the traditionally niche and proprietary product-driven SON market with a host of open standards-compliant RIC (RAN Intelligent Controller), xApp and rApp offerings, which are capable of supporting both near real-time D-SON and non real-time C-SON capabilities for RAN automation and optimization needs.
– The report estimates that global spending on RIC platforms, xApps and rApps will reach $120 Million in 2023 as initial implementations move from field trials to production-grade deployments. With commercial maturity, the submarket is further expected to quintuple to nearly $600 Million by the end of 2025.
– Annual investments in the wider SON market – which includes licensing of embedded D-SON features, third party C-SON functions and associated OSS platforms, in-house SON capabilities internally developed by mobile operators, and SON-related professional services across the RAN, mobile core and transport domains – are expected to grow at a CAGR of approximately 7% during the same period.
– The third party SON vendor ecosystem is exhibiting signs of consolidation, with several prominent M&A deals such as Qualcomm’s recent acquisition of C-SON specialist Cellwize – in a bid to strengthen its 5G RAN infrastructure offerings, Elisa Automate’s merger with Polystar to form Elisa Polystar, and HCL’s acquisition of Cisco’s SON technology business.
– However, on the other hand, newer suppliers are also beginning to emerge – extending from VMware, Juniper Networks and other RIC platform providers to x/rApp specialists such as Cohere Technologies, DeepSig, Groundhog Technologies, Subex, B-Yond, Net AI and RIMEDO Labs.
– SON capabilities are playing a pivotal role in the ongoing proliferation of private 4G/5G networks, as evident from a growing number of cross-sector partnerships. For example, private wireless service provider Betacom is collaborating with Qualcomm to accelerate enterprise adoption of private 5G networks by  combining the former’s 5GaaS (5G-as-a-Service) offering with the latter’s enablement ecosystem, including the Cellwize RAN automation and management platform. Similarly, Germany-based systems integrator Opticoms has entered into a partnership with SON specialist Innovile to automate and optimize Open RAN standards-compliant private 5G networks.
– Over the last two years, with the steep rise of mobile data consumption in residential areas during the COVID-19 pandemic-imposed lockdowns, mobile operators – despite coping relatively well – have recognized the importance of a more dynamic and automated approach to the optimization of network assets in order to provide a consistent and seamless user experience.
– The 2020-2022 period saw large-scale C-SON deployments by several operators, including but not limited to Verizon, EE (BT Group), Orange, Telefónica, Turkcell, beCloud (Belarusian Cloud Technologies), VEON, Ooredoo, Zain, BTC (Botswana Telecommunications Corporation), LTT (Libya Telecom & Technology), Telstra, Singtel, Telkomsel, Globe Telecom, Smart Communications (PLDT), and Telecom Argentina.

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Product Overview
Machine learning is a subset of Artificial intelligence (AI) that enables computers to learn without being carefully programmed. It primarily focuses on promoting computer programs that can be changed when new data is released. It assists the computer analyses and gets meaningful information without being programmed acutely for them. In the modern safety and security technology environment, it has numerous functions such as face detection, face recognition, picture classification, voice recognition, antivirus, Google, and spam filtering, genetic engineering, signal diagnostics, and weather prediction.

Market Highlights
This report anticipates the Machine Learning market to surpass USD XX Billion by 2030; this is valued at XX billion in 2019 at a compound annual growth rate of XX%. Organizations around the world implement machine learning services that deliver better client experience, ROI and create a competitive advantage in the fields of business. In addition, machine learning applications are anticipated to rise progressively in various end user industries in the near term. Progresses in technology and data generation are some of the main drivers in the market.

Recent Highlights in the Machine learning market
In April 2019, The Google AI platform was launched by Google. The platform enables machine learners, both on site and on cloud servers, to build and develop their machine learning models. The framework also includes Google’s machine learning suite including TensorFlow, TPUs, and TFX tools.
In August 2019, Vertex.ai, the launching model suite for AI, was acquired by Intel. The deal aided Intel to develop and extend its portfolio of products through a team that has extensive technology experience in deep learning technology.
Machine Learning market: Segments
Global Machine Learning market has been segmented based on technology, product offering, and application. It has been further segmented based on region into North America, Europe, Asia-Pacific, Mid East, and Africa.

By Application (in %), global Managed Security Services Market, 2019
Healthcare segment is expected to dominate the market with a growing CAGR of XX%
Global Managed Security Services market is segmented by application into: Advertising & media, BFSI, Government, Healthcare, Retail, Telecom, Utilities, and Manufacturing. This is because this technology has been increasingly adopted in new areas of healthcare. This technology is used for example to estimate the probability of a person’s death. The use of machine learning to improve diagnostic knowledge and use it for disease prevention moves the medical field from reactive to proactive, thus augmenting the segment’s growth.

By Solution Type (in %), Global Machine Learning market, 2019
In 2019 the Hardware, solution accounted for XX% of the total Machine Learning market
Global Machine Learning market is segmented by solution type: Software, hardware, and services. During the projected timeframe, the hardware industry is anticipated to record the highest CAGR. This can be driven by the increasing use of machine learning hardware. The development of AI and ML custom silicon chips drives hardware implementation. Departments such as Samba Nova Systems are expected to develop more powerful processing systems to further fuel the market.

Global Machine Learning market: Market Dynamics
Drivers
Collaborative Policies adopted by many companies
The competitive landscape in the market for Machine learning shows a tendency towards companies that adopt strategies such as partnerships, cooperation, and joint ventures as well as launching and developing of their products. With the growing global market growth, companies in this sector are being forced to develop collaborative policies to sustain themselves in the highly competitive market.

Technological advancement and implementation by end user
Organizations around the world implement machine learning services that deliver better client experience, ROI and create a competitive advantage in the fields of business. In addition, machine learning applications are anticipated to rise progressively in various end-user industries in the near term. Progresses in technology and data generation are some of the main drivers in the market.

Restraints
Insufficient Knowledge
One reason for the slow adoption of machine learning in the sector is a lack of necessary knowledge about ML technology among decision-makers. Many companies simply don’t have sufficient technical background to understand how their business can benefit from AI adoption. Conservative stakeholders prefer to stick with time-proven methods and tools rather than risk trying something new.

Fewer number of trained professionals
Machine learning is still a new technology, and skilled professionals are few. There are many experts with a thorough theoretical insight into the topic. Yet it is incredibly difficult to find professionals who are able to develop reliable, functional ML applications.

Global Machine Learning Market: Regions
The largest share of the market in 2019 was held by America which accounting for over XX% share in the world.
Asia Pacific region despite having the lowest market share is anticipated to grow at the highest CAGR during the forecast period.
The expansion of the marketing industries has led to the development of increasing e-commerce, online streaming and spread of internet. Marketers now seek more advanced and cutting-edge marketing solutions for a wide range and ever-growing customer bases. This led to the implementation of Machine learning-compatible marketing solutions. In security, organizations and governments focus on comprehensive defense infrastructure with rising occurrences of cyber-attacks and widespread cyberwar in the region. APAC is considered the largest market for industrial robots, particularly China, Japan and South Korea.

Asia Pacific: China, Japan, India, and Rest of Asia Pacific
Europe: Germany, the UK, France, and Rest of Europe
North America: The US, Mexico, and Canada
Latin America: Brazil and Rest of Latin America
Middle East & Africa: GCC Countries and Rest of Middle East & Africa

Global Machine Learning market is further segmented by region into:
North America Market Size, Share, Trends, Opportunities, Y-o-Y

Growth, CAGR – United States and Canada

Latin America Market Size, Share, Trends, Opportunities, Y-o-Y
Growth, CAGR – Mexico, Argentina, Brazil and Rest of Latin America

Europe Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – United Kingdom, France, Germany, Italy, Spain, Belgium,

Hungary, Luxembourg, Netherlands, Poland, NORDIC, Russia,

Turkey and Rest of Europe

APAC Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – India, China, South Korea, Japan, Malaysia, Indonesia, New

Zealand, Australia and Rest of APAC

MENA Market Size, Share, Trends, Opportunities, Y-o-Y Growth,
CAGR – North Africa, Israel, GCC, South Africa

Global Machine Learning Market: Impact of COVID-19
In response to a disease outbreak of COVID-19 and to continue expanding problems, artificial intelligence (AI), Machine Learning (ML) virtual reality (VR) and augmented reality (AR) technologies are expected to contribute significantly. The present situation due to the epidemic will motivate pharmaceutical providers and health centers, as the key technologies for different initiatives, to strengthen their R&D investments in ML. The cost-efficiency pressures are anticipated in the insurance sector. In the course of the renewal process, the use of AI can drastically reduce operating cost and, at the same time, can improve customer satisfaction. ML, VR / AR can support Digital-learning, which will be increasingly demanded by the shutdown of many schools and institutions. VR / AR can also provide offline assistance as they help minimize potential journeys and can prove to be a worthwhile solution.

Machine learning market: Key Players
IBM Corporation
Company Overview
Business Strategy
Key Product Offerings
Financial Performance
Key Performance Indicators
Risk Analysis
Recent Development
Regional Presence
SWOT Analysis
SAP SE
Microsoft Corporation
Huawei Technologies
HCL Technologies
Accenture Plc
Schneider Electric
Honeywell International
Rockwell Automation
Schlumberger Limited
Global Machine Learning market report also contains analysis on:
Machine Learning market Segments:
By Application:
Advertising & media
BFSI
Government
Healthcare
Retail
Telecom
Utilities
Manufacturing
By Solution Type:
Software
Hardware
Services
Machine Learning market Dynamics
Machine Learning market Size
Supply & Demand
Current Trends/Issues/Challenges
Competition & Companies Involved in the Market
Value Chain of the Market
Market Drivers and Restraints
Related Reports: Global Dental Sutures Market Analysis & Forecast 2016-2027

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Global market insights:
The global machine learning market is expected to reach a value of INR 543.18 Bn by the end of 2023, from INR 91.83 Bn in 2017, expanding at a compound annual growth rate (CAGR) of nearly 42.99%. This growth is attributed to technological advancements, and its proliferation in the field of data generation.

India market insights:
In India, though the adoption of AI applications is still at a nascent stage, it is anticipated that the market will grow at a high rate in the coming years. Among the various AI technologies being implemented in India, the highest investments are in the field of machine learning applications. In India, investments in artificial intelligence (which was INR 773.64 Bn in 2017) is expected to expand at a CAGR of 33.49% during the 2018-2023 period. The reason behind such high anticipated investments is the growing popularity of AI technologies, especially ML applications, across business functions like finance, accounting, general management, IT, operations, and administration.
Key growth drivers of the market:
o The implementation of machine learning reduces the chances of human error. Also, it makes machines smarter, thereby reducing their reliance on operators. In a way, machine learning enhances technicians’ accuracy and efficiency, owing to which many companies are switching from traditional data interpretation methods, to machine learning. This, in turn, is providing growth opportunities for the market in India.
o Cloud-based analytics, when used in conjunction with machine learning, helps analysts identify anomalies in real time, so that they can be rectified swiftly. The introduction of the concept of stream-based analytic platforms has opened up new avenues for the extensive use of machine learning applications in various sectors of the Indian market.

Key deterrents to the growth of the market:

Most machine learning applications are interlinked with cloud-based analytics. In order to update customer information on cloud-based platforms, front line employees ask customers to provide their personal details (such as social security numbers, financial information, etc.). However, customers are often reluctant to share such information, owing to concerns regarding privacy. Customers’ apprehensions regarding the safety of their information hinders the implementation of machine learning applications.

Companies covered:

o Artivatic Data Labs Pvt. Ltd.
o Formcept Technologies & Solutions Pvt. Ltd.
o PixelCrayons
o QBurst Technologies Pvt. Ltd.
o Active Intelligence Pte. Ltd.
o Amazon Web Services, Inc.
o Fair Isaac Corporation
o International Business Machines Corporation
o Microsoft Corporation
o Xoriant Corporation

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Global Computer Vision Market Analysis and Forecast, 2017-2023
? To provide a detailed analysis of the market structure along with a forecast of the various segments and sub-segments of the computer vision market
? To provide insights into factors affecting market growth
? To analyze the computer vision market based on Porter’s Five Forces analysis
? To provide historical and forecast revenue of the market segments and sub-segments with respect to four main regions and their countries??North America, Europe, Asia-Pacific, and the rest of the world
? To provide country-level analysis of the market with respect to the current market size and future perspective
? To provide country-level analysis of the market for a segment on the basis of component, applications and vertical.
? To provide strategic profiling of the key players in the market, comprehensively analyzing their core competencies, and drawing a competitive landscape for the market
? To track and analyze competitive developments such as joint ventures, strategic alliances, mergers & acquisitions, new product developments, and research and development in the computer vision market
Target Audience
? Semiconductor product designers and fabricators
? Developers of machine vision-related applications
? Providers of machine vision-related components and solutions
? Technology standard organizations, forums, alliances, and associations
? Technology investors
? Universities and research organizations
? Government bodies
Key Findings
? The global computer vision market is expected to reach USD 48.32 billion by 2023.
? The hardware segment was valued USD 4.7 billion in 2016 and is expected to reach USD 26.43 billion with a 29.19% CAGR by 2023. The software segment is projected to be the fastest growing segment and is presumed to reach around USD 21.89 billion with 35.13% CAGR by 2023.
? Gesture recognition accounted for the largest market share in 2017, with a market value of USD 2,957 million and is projected to grow at a CAGR of 34.74% during the forecast period. The face recognition segment was the second-largest market in 2017, valued at USD 2991.3 million in 2017; it is projected to register a CAGR of 30.07%.
? The automotive segment accounted for the largest market share in 2017, with a market value of USD 1626.7 million and is projected to exhibit a CAGR of 33.86% during the forecast period. The entertainment segment was the second-largest market in 2017, valued at USD 1,805.9 million in 2017; it is projected to register a CAGR of 35.27%.
? The market in North America is projected to be the largest during the assessment period, followed by Europe. Asia-Pacific is expected to be the fastest growing market during the forecast period.

Regional and Country-Level Analysis of the Computer Vision Market, Estimation and Forecast
North America is expected to be the largest revenue generating region for global computer vision market during the forecast period. This growth is attributed to the increasing demand from the gaming industry and increase in the adoption rate of technology in mobile devices. Moreover, the presence of IT giants which are involved in emerging technologies such as augmented reality and virtual reality are expected to drive the market during the forecast period. Europe stands second in the computer vision market due to the increased investments in evolving artificial intelligence and regulatory compliances in providing pre-requisite infrastructure for SMEs to adopt AI. Asia-Pacific is expected to be the fastest growing market registering a CAGR of 35.95% owing to the developing AI talent and encouraging usage at the domestic level and the increasing demand for computer vision from the media and entertainment industry.
The report also offers a brief country-level analysis for the various regional markets.

? North America
o US
o Canada
o Mexico
? Europe
o UK
o Germany
o France
o Rest of Europe
? Asia-Pacific
o China
o Japan
o India
o South Korea
o Rest of Asia-Pacific
? Rest of the World
o Middle East
o Africa

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The value of the machine learning market in Asia-Pacific is expected to reach USD 10.00 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 51.3% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
Asia-Pacific will experience the highest CAGR in the machine learning market.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into China, India, Japan, South Korea, Australia and New Zealand (ANZ), the rest of Asia-Pacific.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
Machine learning is no longer a novelty in Asia-Pacific countries. Business sectors having realised its potential are using machine learning technologies to draw maximum insights from the available data to increase the efficiency of operations.

Key growth factors

The enormous population base together with a diverse industry mix, which has the potential to generate a huge amount of data, is significantly driving the machine learning market in the Asia-Pacific countries.
The availability of a robust data set, the adoption of machine learning techniques in traditional industries and strengthening of the pipeline of cohorts with exceptional talent is driving the machine learning market in the Asia-Pacific region.

Threats and key players

Ethical issues and biased data leading to biased decisions are a matter of concern which restricts further development of the machine learning market.
The connectivity standards available in the Asia-pacific region still falls below the worlds average. The digital divide is widening the gap among the sub-regions in the Asia-Pacific zone at an alarming rate. This again is causing a hindrance to the development in the machine learning market in this region.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

What is covered in the report?

1. Overview of the machine learning market in Asia-Pacific region.
2. Market drivers and challenges in the machine learning market in Asia-Pacific region.
3. Market trends in the machine learning market in Asia-Pacific region.
4. Historical, current and forecasted market size data for the machine learning market in Asia-Pacific region.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (China, India, Japan, South Korea, Australia and New Zealand (ANZ), the rest of Asia-Pacific) market size data for machine learning market.
10. Analysis of the machine learning market in Asia-Pacific by value chain.
11. Analysis of the competitive landscape and profiles of major compet

The post Asia-Pacific Machine Learning Market (2018-2023) first appeared on CRI Report.

The value of the machine learning market in Europe is expected to reach USD 3.96 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 33.5% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
Europe stands in the second position after North America in the machine learning market.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into the European Union five (EU5), rest of Europe.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
The trend of supporting, educating, enforcing and steering the economy towards a machine learning-friendly environment is seen to be followed throughout Europe.
European countries are successfully bridging the gap between additional renewable energy and excess power into the grid by making ultra-accurate forecasts of the demand and supply in real time by making use of the machine learning technologies, thereby saving energy and cost.

Key growth factors

The world-class research facilities, the emerging start-up culture, the innovation and commercialisation of machine intelligence technologies is giving thrust to the machine intelligence market in Europe.
Amongst all regions, Europe has the largest share of intraregional data flow. This, together with the machine learning technologies, is boosting the market in Europe.
The excessive usage of the machine learning technology across economy in all facets of businesses is proving to be a big thrust to the machine learning market. Profound usage has been found in sectors such as agriculture, healthcare and media for optimisation of prices and carrying out predictive maintenance in manufacturing.

Threats and key players

Investors in Europe are more concerned about the ROI from investing in the machine learning market. The adoption of machine learning by the start-ups is a farce in Europe since research suggests that only 5% of the start-ups investing in machine learning end up with a revenue of more than $50 Mn in revenue. Also, opportunities for external investments are bleak.
The machine learning market is in a stage of infancy; there is a lacuna between the skills required and that which is inherent in the workers. It requires a considerable amount of time to pick up the skills. Also, the Europeans are concerned about the penetration of machine learning into their lives, and how it is going to impact employment in the country. Concerns environing these factors are hindering the further developments in the machine learning market.
Given that machine intelligence depends on the easy availability of data, the practice of data minimisation and data privacy standards act as a barrier to the further development of the machine learning market in Europe.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, Intel, Facebook and Apple.

What is covered in the report?

1. Overview of the machine learning in Europe.
2. Market drivers and challenges in the machine learning in Europe.
3. Market trends in the machine learning in Europe.
4. Historical, current and forecasted market size data for the machine learning market in Europe.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (the European Union five (EU5), rest of Europe) market size data for machine learning market.
10. Analysis of machine learning market in Europe by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.

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The value of the machine learning market in Latin America is expected to reach USD 0.93 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 24.8% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
Latin America is fast developing in the field of machine learning.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into Brazil, Mexico, Argentina, the rest of Latin America).
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
A notable trend of using machine learning in the health service sector is observed. The doctors use machine learning technologies to measure the likelihood of patients suffering from zika, dengue fever or chikungunya in order to prevent future outbreaks.
Machine learning has found its way into all kinds of industries. The Brazilian stock exchange makes intensive use of machine learning technologies to order out the chaos and put the theory into practice.

Key growth factors

Latin Americas drive towards digital economy has led to the companies transforming into intelligent enterprises improve business processes and install intelligent machines to take up routine work.

Threats and key players

75 to 81 percent of the people in Latin America possess low or medium skills. The less skilled and less educated will find it more difficult to pick up the skills necessary for the machine learning industry. This, therefore, acts as a hindrance to the further development in the machine learning market.
The lack of understanding as to why technology is essential is partially present. Theres also a belief that technology is not mature enough to give fruitful benefits from its utilisation. Also, the availability of proper data sets to use in the process of machine learning technologies is absent.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, and Intel.

What is covered in the report?

1. Overview of the machine learning in Latin America.
2. Market drivers and challenges in the machine learning in Latin America.
3. Market trends in the machine learning in Latin America.
4. Historical, current and forecasted market size data for the machine learning in Latin America.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (Brazil, Mexico, Argentina, The rest of Latin America) market size data for machine learning market.
10. Analysis of machine learning market in Latin America by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.

The post Latin America Machine Learning Market (2018-2023) first appeared on CRI Report.

The value of the machine learning market in Middle East and Africa is expected to reach USD 0.50 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 29.1% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into The UAE, Saudi Arabia, South Africa, Rest of Middle East and Africa.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
The use of machine learning in healthcare has gained grounds in recent times. The hospitals in the Middle East are making use of the machine learning technologies for making a diagnosis of the diseases that may crop up in future, and for more precise analysis, prevention and treatment of individuals.

Key growth factors

The labour intensive sectors like retail and healthcare have taken up the use of machine learning to provide better consumer experiences. The urge for automation in these sectors is driving the machine learning market in the Middle East and Africa.
The high growth in the start-up culture with the government encouraging innovation has led them to make ample amount of investments in machine learning technologies, which in turn is driving the machine learning market.
Threats and key players

The adoption of machine learning in all the industries is going to be a slow process in Africa until and unless infrastructure and consumer spending power improves.
The unavailability of cohorts with adequate machine learning skills act as a barrier to the further development in the machine learning market.
The key players are Google Inc., Microsoft, IBM Watson, Amazon, and Intel.

What is covered in the report?

1. Overview of the machine learning market in the Middle East and Africa.
2. Market drivers and challenges in the machine learning in the Middle East and Africa.
3. Market trends in the machine learning in the Middle East and Africa.
4. Historical, current and forecasted market size data for the machine learning market in the Middle East and Africa.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others)
9. Historical, current and forecasted regional (The UAE, Saudi Arabia, South Africa, Rest of the Middle East and Africa) market size data for machine learning market.
10. Analysis of machine learning market in the Middle East & Africa by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.

The post Middle East and Africa Machine Learning Market (2018-2023) first appeared on CRI Report.

The value of the machine learning market in North America is expected to reach USD 8.07 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 43.4% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
North America has the most significant share of the machine learning market.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into the U.S. and Canada.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.

There is a notable trend of using machine learning models in the media and entertainment industry. This is done to better the customer experiences, keep them engaged by providing them relevant content and make a drive towards greater viewer personalisation.
Insurance companies in North America are using machine learning algorithms to access the market trends for maximising business opportunities. Similarly, prospects of new developments in the product market are informed to the clients by applying machine learning algorithms to the available client data.
The tech-savvy consumers in North America are providing a myriad of opportunities to this machine learning market. The urge for exact prediction in all facets of life has pushed every organisation, irrespective of the industry they belong to, to use data to drive value and to provide more personalised user experience.

Key growth factors

The outbreak of mobile computing systems in the last decade, which paved the way for easy collection and transmission of data across platforms, has led to the emergence of big data on which machine learning is hugely dependent now, giving a boost to the machine learning market in North America.
Predictive analytics help in taking preventive measures for probable health emergencies by looking at the data for key health indicators. This drives machine learning in the hospitals of North America.

Threats and key players

There is a detachment between the actual potential and how machine learning is being used in the real world. A lot of research and development (R&D) is being done to push the boundary a bit further. Inability to realise the full value, the made investments may give a severe backlash to this machine learning market in North America.
The key players are Microsoft, Google Inc., IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

What is covered in the report?

1. Overview of the machine learning market in North America.
2. Market drivers and challenges in the machine learning in North America.
3. Market trends in the machine learning in North America.
4. Historical, current and forecasted market size data for the machine learning in North America.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (US, Canada) market size data for the machine learning market.
10. Analysis of North America machine learning market by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.

The post North America Machine Learning Market (2018-2023) first appeared on CRI Report.

The value of the machine learning market is expected to reach USD 23.46 Bn by 2023, expanding at a compound annual growth rate (CAGR) of 42.6% during 2018-2023.
Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.
North America has the most significant share of the machine learning market, while Asia-Pacific is expected to witness the highest CAGR.

The market can be classified into four primary segments based on components, service, organization size and application.
Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa.
Based on components the market can be segmented into software tools, cloud and web-based application programming interfaces (APIs) and others.
Based on service, the sub-segments are composed of professional services and managed services.
Based on organization size, the sub-segments include small and medium enterprises (SMEs) and large enterprises.
Based on application, the market is divided into the sub-segments, banking, financial services and insurance (BFSI), automotive, healthcare, government and others.
The trend of using machine learning techniques in the healthcare sector, financial sector and retail sector are widespread. The world is moving towards a connected business world to make data-powered decisions.

Key growth factors

A large amount of data that is generated by the industries provide an impetus to this market. Also, an increased usage of deep learning techniques in the various industries is also one of the reasons giving thrust to the market. A lot of research and development (R&D) is done to improve the efficiency of the output provided by the machine learning market.
Technological advancement, proliferation of data and the dire need to derive maximum information from the available data have been identified as the key reasons for the growth in this market.

Threats and key players

Although an enormous amount of money has been spent, there remains an uncertainty environing how the deep training net works. Also, professionals are not equipped with adequate machine learning skills in the market. Wrong program formulations would lead to biased results, leading to difficulty in analysis.
The inefficiency of the cloud infrastructures in the developing countries, which are needed to store and seamlessly access data, act as a hurdle to the growth in this market.
The key players are Google Inc., Microsoft, IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

What is covered in the report?

1. Overview of the machine learning market.
2. Market drivers and challenges in the machine learning market.
3. Market trends in the machine learning market.
4. Historical, current and forecasted market size data for the machine learning market.
5. Historical, current and forecasted market size data for the components segment (software tools, cloud and web-based APIs and others).
6. Historical, current and forecasted market size data for the service segment (professional services and managed services).
7. Historical, current and forecasted market size data for the organisation size segment (SMEs and large enterprises).
8. Historical, current and forecasted market size data for the application segment (BFSI, automotive, healthcare, government and others).
9. Historical, current and forecasted regional (North America, Europe, Asia-Pacific, Latin America, the Middle East & Africa) market size data for machine learning market.
10. Analysis of the global machine learning market by value chain.
11. Analysis of the competitive landscape and profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for machine learning to determine the viability of the market.
2. Determine the developed and emerging markets for machine learning.
3. Identify the challenge areas and address them.
4. Develop strategies based on the drivers, trends and highlights for each of the segments.
5. Evaluate the value chain to determine the workflow.
6. Recognize the key competitors of this market and respond accordingly.
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide on the direction of further growth.

The post Global Machine learning market (2018-2023) first appeared on CRI Report.