Machine Learning Market Size, Share & COVID-19 Impact Analysis, By Component (Solution, and Services), By Enterprise Size (SMEs, and Large Enterprises), By Deployment (Cloud and On-premise), By Industry (Healthcare, Retail, IT and Telecommunication, BFSI, Automotive and Transportation, Advertising and Media, Manufacturing, and Others), and Regional Forecast, 2020-2027

The global machine learning (ML) market size stood at USD 8.43 billion in 2019 and is projected to reach USD 117.19 billion by 2027, exhibiting a CAGR of 39.2% during the forecast period.

In the global effort to attenuate the spread of novel Coronavirus, several countries have adopted quarantine measures and social distancing policies to mitigate the impact of the pandemic. The developers and researchers intend to use such tools to analyze the effects of these measures.

• For instance, in April 2020, the researchers at Massachusetts Institute of Technology developed a model that uses the data from COVID-19 pandemic. The model leverages machine intelligence algorithms to determine or predict the spread of virus and efficacy of quarantine measures. Such advancements are likely to surge the demand for advanced machine intelligence capabilities.

Advancements in deep learning and integration of machine intelligence capabilities with analytics-driven solutions are expected to be the growing market trend. Substantial investments in artificial intelligence (AI) and the advent of self-driving cars are some of the factors that influence machine learning market growth.

LATEST TRENDS

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Advancements in Deep Learning to Push Market towards Higher Growth Trajectory

The field of machine intelligence is experiencing exponential growth across the globe. Deep learning is a subset of ML in artificial intelligence (AI) technology. Deep learning leverages the hierarchical level of artificial neural networks (ANN) to carry out the process of machine learning. The global market is expected to gain traction owing to the advancements in the deep learning algorithms.

Many companies are strengthening their deep learning capabilities to drive innovation. For instance, in November 2019, Panasonic Corporation announced the offering of its application programming interface (API) for facial recognition technology incorporated with deep learning capabilities.

Integration of Machine Intelligence with Analytics-driven Solutions to Bolster Growth

Retail analytics has witnessed significant growth in recent years. Many e-commerce companies such as Amazon, Alibaba, and eBay are leveraging advanced data analytics solutions to boost their sales and provide enhanced customer experience. Research and development in speech and voice recognition technology have led to the emergence of cognitive speech coding techniques using ML paradigms.

ML in security analytics is likely to be a fast-growing trend. ML in cybersecurity plays a crucial role. Some of the established players have implemented machine intelligence solutions to protect their data. For example, Google is leveraging machine intelligence solutions to mitigate threats against mobile end-points running on Android. Thus, the above data shows that the integration of advanced learning capabilities with analytics-driven solutions is likely to be a growing market trend.

DRIVING FACTORS

Substantial Investments in Artificial Intelligence (AI) to Drive Market

The commercial applications of AI are proliferating in both advanced and emerging economies. As a result of this, there is an intense competition between the market players to fund, develop, and acquire artificial intelligence technology startups. For instance, May 2020 Intel Corporation announced capital investments of USD 132 million in 11 disruptive technology startups. The startups including California based Anodot, that leverages ML to drive autonomous business monitoring. Similarly, Hypersonix, a provider of AI-powered autonomous analytics platform. KFBIO, the provider of digital pathology systems, Xsight Labs, develops innovative technology for machine intelligence, data analytics, and disaggregated storage among others. Such heavy investments would ensure to drive the global market in the coming years.

Advent of Self-Driving Cars to Augment Need for Advanced Learning Solutions

Self-driving cars have gained tremendous popularity in the recent years. The advancements in artificial intelligence technology and the Internet of Things (IoT) have paved way for driverless cars.ML is a vital component of the centralized electronic control unit (ECU) in a self-driving car. Real-time monitoring of the surroundings and predicting possible changes in the environment are the primary functions of ML algorithms in self-driving cars.

ML algorithms such as supervised learning, unsupervised learning, pattern recognition algorithms, decision matrix algorithms, among others are integrated into the functioning of self-driving cars. For example, Waymo LLC an autonomous driving technology development company headquartered in California, uses categorical machine intelligence technology to classify and detect different types of road features and objects. By incorporating advanced ML algorithms, the company intends to expand to more cities to encounter new scenarios and objects. These factors are likely to fuel the demand for intelligent retrieval algorithms, thereby aiding to the expansion of the market. 

RESTRAINING FACTORS

Increasing Emphasis on Data Privacy to Restrict Market Growth

Deepfakes posed by AI-enabled fake content are suspected to be a national security threat. However, the idea of AI licenses can overcome this constraint. For instance, Responsible AI licenses (RAIL) enable the software developer to publish open-source ML software with a license. This mitigates the risk of software to be used illegally.

Also, ML in robotic automation processes is exposed to fraud and undesired use cases which may hinder growth. To prevent the misuse of robot’s governments across, all regions are taking preventive measures. For instance, in September 2018, California approved a bill namely ‘California B.O.T. Act of 2018’. The law would criminalize the use of bots to interact with the citizen of California if the bot intents to mislead the person.

MARKET OPPORTUNITY

Spread of COVID-19 Pandemic Across Globe to Create Unprecedented Market Opportunities for Companies

The application of artificial intelligence technology is likely to help combat the COVID-19 pandemic. Several countries are using population surveillance methods to track and trace COVID-19 cases. For example, in South Korea, researchers are using surveillance-camera footage, geo-location data, to track corona patients. By using this data, scientists use machine intelligence algorithms to predict the location of the next outbreak and inform the responsible authorities to help track the disease in real-time. Such active initiatives are likely to surge the demand for machine intelligence solutions in the coming period.

SEGMENTATION

By Component Analysis

Machine Learning Solutions to Account for Major Market Share

By component, the market is segmented into solutions and services. The solution segment is expected to hold major share during the forecast period. The solutions provided by the market players include IBM Corporation’s Watson ML, Oracle ML, Microsoft Corporation’s Azure ML among others. These solutions help data scientists and researchers to accelerate the deployment of artificial intelligence and ML. Hence, solutions are expected to hold a major share. Machine-learning-as- a service is expected to be a growing market trend in the coming years.

Professional and managed services are considered under the services segment. The services are forecast to witness significant growth during the forecast period. These services help industries in making informed decisions, actionable strategies; enhance operation workflow, end-to-end risk, and compliance management capabilities.

By Enterprise Size Analysis

Small and Mid-Sized Enterprises to Increase their Tech Expenditure to Deploy AI and ML Technologies

By enterprise size, the market is bifurcated into small and mid-sized enterprises (SME’s) and large enterprises. Small and mid-sized enterprises are forecast to exhibit a significant growth rate during the forecast period. AI and ML, are expected to be the key technologies that will enable the SME’s to reduce ICT investments and access digital resources. Large enterprises are likely to hold a major machine learning market share.

By Deployment Analysis

Increasing Demand for adoption of Cloud Based Solutions to Drive Market

By deployment, the market has been classified into the cloud and on-premise. The market players offer machine intelligence solutions that can be deployed on the cloud or on-premise. For instance, Alphabet, Inc. provides Google Cloud. The Google cloud offers a comprehensive toolset regarding ML and AI. Similarly, BigML offers on-premise deployments for the enterprises that wish to configure, maintain, and manage their installations within the enterprise. The cloud-deployments are expected to witness a remarkable growth rate during the forecast period.

By Industry Analysis

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Healthcare Industry to Witness Remarkable Market Growth Owing to Increasing Demand for Real-Time Health Monitoring

The industries working with large amounts of data leverage ML technology to glean real-time insights from the data. The real-time insights enable the industries to gain more advantage over competitors and work more efficiently.

In the pre-COVID scenario, the advent of wearable devices and sensors that are used to assess the patients’ health in real-time has surged the demand for machine intelligence applications in the healthcare industry. The technology helps medical experts to identify trends and analyze data to improve diagnosis and treatment. However, the outbreak of novel Coronavirus has increased the applications of artificial learning technology across the sector.

Banks and other financial businesses leverage machine intelligence technology to prevent fraud and identify important insights in data. E-Commerce has proved to be a major driver for retail trade business practices. Retailers rely on machine intelligence to capture data, analyze it, and leverage it to offer personalized shopping experience to customers. These are some of the aspects that drive the demand for ML technology in the financial and retail industries.

In the pre-COVID period, the manufacturing industry had been demonstrating an impressive adoption of artificial intelligence technology. Industrial Internet of Things (IIoT), Industry 4.0, smart factory including smart robotics, intelligent logistics, autonomous delivery using drones, and intelligent inventory management are some of the strong use cases of ML in the manufacturing industry.

Automotive and transportation segment is projected to witness significant growth in the coming years. Research & development in self-driving cars, and autonomous transportation, are driving the demand for advanced solutions. In the advertising and media industry, the proliferation of over-the-top (OTT) services has influenced OTT providers such as Netflix, Hotstar, and Amazon.com among others to deploy deep learning and machine intelligence to gain deep customer insights.

Further, machine intelligence has gained importance in the education sector owing to advanced programs such as Education 3.0.  In the energy and utilities segment, industry prediction of energy consumption is the most popular use case of machine learning. Smart grid distribution drives the need for advanced algorithms in the sector.

REGIONAL INSIGHTS

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Asia Pacific to Experience Flourishing Growth Rate During the Forecast Period Backed By Vibrant Startup Ecosystem

The market has been categorized into five major regions including North America, Europe, Asia Pacific, the Middle East and Africa, and Latin America. These regions are further divided into countries. North America is anticipated to emerge dominant in the global market. Asia Pacific is expected to witness a high growth rate.

North America is expected to hold major market share owing to the presence of R&D investors such as IBM Corporation, Amazon.com, and Oracle Corporation across the region. Moreover, heavy investments and the availability of established IT infrastructure are expected to drive the market growth in North America. For instance, the defense advanced research projects agency (DARPA) of the U.S. invested USD 2 billion to develop AI technologies such as ML among others.

In June 2018, the European Union proposed a Digital Europe Program with a budget of 10.4 billion USD for the period 2021-2027. The program aims at advancing AI technology and deploying its applications across the society and economy. Such active initiatives are likely to create new market opportunities and augment the regional market growth.

Asia Pacific is forecast to exhibit a fascinating growth rate. The developing economies in the region such as China, India, and Japan pose a strong and vibrant startup ecosystem assisted with an increasing number of skilled workforce that drives the market growth across the region. Further, the government of Singapore has created an AI Ethics Advisory Council as a part of its AI Strategy to deploy the applications of AI across different sectors. These are some of the factors that drive market growth in the Asia Pacific.

In the Middle East and Africa, the oil-rich Gulf States are making active efforts to diversify their economies through artificial intelligence. For instance, in 2017 Saudi Arabia gave citizenship to a robot. Whereas, the United Arab Emirates announced its first dedicated artificial intelligence minister. Also, smart city initiatives, and autonomous transport drive the demand for AI capabilities in the region. The Latin American countries such as Brazil, Mexico, and Uruguay are developing new AI policies and coherent strategies. The region is expected to pose new lucrative market opportunities in the future.

KEY INDUSTRY PLAYERS

Microsoft Corporation to Introduce New Capabilities in its Product Offerings to Strengthen the Market Position

Microsoft Corporation offers automated machine intelligence solutions to easily build learning models and drive innovation. Microsoft’s Machine Learning Operations (MLOps) or DevOps incorporates the Azure’s ML capabilities. Responsible ML is the company’s state-of-the-art technology that empowers data scientists to innovate responsibly. To strengthen their market position, Microsoft Corporation tends to introduce new capabilities in its product offerings. For instance,

• October 2019 – Microsoft Corporation announced automated machine learning (AutoML) in power business intelligence (BI) available in public cloud regions. AutoML in Power BI empowers business analysts to build ML models to resolve corporate difficulties.


• March 2019 – Microsoft announced new and advanced capabilities to its Azure and Microsoft 365 platform.  The new capabilities include the integration of ML with Azure's security center to reduce the attack surface of the internet-facing virtual machines.

High Performers to Enter into Strategic Partnership & Collaborations to Drive Innovation

The activities of high performers such as SAP SE, IBM Corporation, SAS Institute, Inc., and Amazon.com, Inc. among others and niche market players including H2O.ai, and Fair Isaac Corporation, among others are focused on partnership and collaboration activities. These strategies will enable the companies to expand the product line and achieve organizational goals. For instance,

• November 2018 – SAP SE acquired Contextor SAS, provider of design, and incorporation of RPA, robotic process automation headquartered in Orsay, France.  Through this acquisition SAP SE aims to drive the expansion and development of its Leonardo machine learning solutions.


• October 2018 – IBM Corporation and Nvidia Corporation collaborated to incorporate Nvidia's RAPIDS, a suite of software libraries built on the companies CUDA-X AI platform, into IBM's enterprise-grade data science platform. The data science platform is specifically for hybrid, on-premise, and multi-cloud environments. The incorporation of RAPIDS will bring GPU acceleration competences to IBM offerings that leverage open source machine software including Graphistry, Anaconda, PyData, INRIA, and Ursa Labs.


• November 2019 - Fair Isaac Corporation announced the release of the Machine-Learning Cyber risk score on the Amazon web services marketplace. AWS marketplace place is a digital catalog of multiple software. Fair Isaac Corporation scoring algorithm leverages micro signal data to quantify the risk of organization cyber-attack risks.


• April 2019 – H2O.ai, entered into a strategic collaboration with Intel Corporation on Project BlueDanube. The project is focused on facilitating H2O.ai machine learning technology on Intel platforms including Intel 2^nd generation Xeon scalable processors.

List of Key Companies Profiled:

• Amazon Web Services, Inc. (Washington, United States)


• Baidu, Inc. (Beijing, China)


• BigML, Inc. (Corvallis, Oregon, United States)


• Fair Isaac Corporation (California, United States)


• Google LLC (California, United States)


• H2o.AI (California, United States)


• Hewlett Packard Enterprise Development LP (California, United States)


• IBM Corporation (New York, United States)


• Intel Corporation (California, United States)


• Microsoft Corporation (Washington, United States)


• Oracle Corporation  (California, United States)


• SAP SE (Walldorf, Germany)


• SAS Institute, Inc. (North Carolina, United States)

KEY INDUSTRY DEVELOPMENTS:

• June 2019 – IBM Corporation announced AutoAI, a set of enhanced capabilities for Watson Studio. AutoAI facilitates the automation of manual tasks associated with optimizing, designing, and governing AI in the enterprise. These solutions also help the data scientist to deploy machine learning models at work. 


• April 2019 – SAS Institute, Inc. enhanced its SAS analytics platform to drive digital transformation. The enhanced SAS analytics platform delivers additional artificial intelligence (AI) capabilities specifically in the areas of natural language processing (NLP), computer vision, and adoption of machine learning among others.

REPORT COVERAGE

The market research report highlights leading regions across the world to offer a better understanding of the user. Furthermore, the report provides insights into the latest industry, current machine learning industry trends and analyzes various technologies that are being deployed rapidly at the global level. The report examines various paradigm shifts associated with the transformation of software. It further highlights some of the growth-stimulating factors and restraints, helping the reader to gain an in-depth knowledge about the market.

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