Machine Learning as a Service (MLaaS) Market

Market Overview

The global machine learning as a service (MLaaS) market size was valued at USD 3.14 billion in 2022. It is estimated to reach USD 61.01 billion by 2031, growing at a CAGR of 39.05% during the forecast period (2023–2031).

A group of services known as "machine learning as a service" (MLaaS) provides machine-learning technologies as a component of cloud computing services. MLaaS is a collection of generic machine learning services that may be customized to fulfill the needs of any enterprise. These services are frequently ready-made solutions. Data visualization, face recognition, APIs, natural language processing, predictive analytics, and deep learning are among the capabilities offered by these services from suppliers. The calculation itself is handled by the data centers run by the provider.

The allure of these services is that, like any other cloud service, clients can get started using machine learning immediately without setting up servers or installing software. Learning tools are included in these services, which numerous cloud providers like Microsoft, Amazon, and IBM provide. A limited trial of MLaaS is frequently provided so that developers can test it out before committing to a platform and becoming familiar with it.

Highlights

• Marketing and advertisement dominate the application segment
• Large Enterprises dominate the organization size segment
• BFSI dominates the end-user segment
• North America is the highest shareholder in the global market

Market Dynamics

Global Machine Learning as a Service (MLaaS) Market Drivers

Increasing Adoption of IoT and Automation

IoT operations ensure that thousands of devices run correctly and safely on an organizational network and that data is accurate and timely. Some IoT platform vendors are using machine learning to manage large IoT systems. Machine learning algorithms can examine large amounts of IoT data to find hidden patterns. In addition, ML inference may automate important operations with statistically generated actions. ML-based IoT data modeling solutions automate the model selection, coding, and validation, eliminating tedious work.

IoT can speed up machine learning for small enterprises. MLaaS suppliers can run more queries faster and do more analysis to extract meaningful data from massive IoT network data caches. More firms use machine learning for data analytics as they implement IoT solutions. Therefore, MLaaS should spur IoT innovation. In addition, Ericsson predicted a 13% CAGR from 12.4 billion IoT connections in 2020 to 26.4 billion in 2026. MLaaS integrates with many sensors and will play a major part in IoT and automation, driving market growth.

Dynamic Nature of the Retail Industry

Agility and improved client interactions are required in the constantly evolving retail industry. Merchants are employing machine learning services to provide customers with exceptional shopping experiences. Wealthy retailers are hiring analytical consulting organizations to obtain insights that are helpful for marketing objectives. Many smaller shops are hopping on the cloud-based machine learning bandwagon to use data to understand their customers better. For participants in the machine learning as a service sector globally, these insights are translating into increased potential. Machine learning also enables the systems or software to automatically learn from every campaign delivered to a consumer, using that learning or previous data in the subsequent iteration. Therefore, the factors above boost the market growth.

Global Machine Learning as a Service (MLaaS) Market Restraint

Need for Skilled Professionals

Building an in-house machine learning infrastructure from scratch is challenging and requires significant investment decisions. Companies may hire highly trained personnel, enable computational systems with high processing power and ability to handle huge amounts of data, and assemble specialists to operate in the field to have an adequate infrastructure to support ML algorithms. The absence of these is acting as a barrier to the expansion of the market.

Global Machine Learning as a Service (MLaaS) Market Opportunities

Increasing Adoption of Cloud-based Services

Multiple businesses are rapidly beginning to adopt machine learning-as-a-service in their technology stacks for several reasons, the main one being the need for company-wide digital transformation. There are several benefits of deploying machine learning on the cloud. Firstly, the cloud's pay-per-use model is convenient for small enterprises' AI or machine learning workloads. Secondly, the cloud makes it easier for enterprises to experiment with their ML capabilities and scale up as the projects go into production and demand increases.

The cloud-based services eliminate the company's need to invest in heavy working and expensive machine learning systems they do not use daily. The pay-per-use cloud computing model is ready to assist businesses save expenses as machine learning or AI workloads expand. Small-sized companies experiment with machine learning and its capabilities and do not initially deploy it completely. By using machine learning on the cloud, enterprises first test and deploy smaller projects on the cloud and then scale up as required, thereby driving the usage of MLaaS. Such factors create opportunities for market growth.

Regional Analysis

Based on region, the global machine learning as a service (MLaaS) market is bifurcated into North America, Europe, Asia-Pacific, and Rest-of-the-World.

North America Dominates the Global Market

North America is the most significant global machine learning as a service (MLaaS) market shareholder and is estimated to exhibit a CAGR of 38.46% during the forecast period. This is due to its robust innovation ecosystem backed by strategic federal investments in cutting-edge technology and the presence of visionary scientists and entrepreneurs from around the world and renowned research institutions, which have fueled the development of MLaaS. Moreover, 5G, IoT, and linked gadgets are significantly increasing in the region. As a result, through network slicing, virtualization, novel use cases, and service requirements, communications service providers (CSPs) must effectively manage an ever-increasing complexity. MLaaS solutions are expected to be driven by this since traditional network and service management tactics are not viable.

Furthermore, the cloud is redefining the regional market for machine learning, and serverless computing enables developers to launch ML applications quickly. Information services mostly drive the ML-as-a-service sector. The most notable change serverless computing has brought is eliminating the need to scale physical database hardware. Such trends allow vendors to introduce ML-as-a-service to simplify the adoption of ML in enterprise adoption and SMEs.

Europe is expected to grow at a CAGR of 38.61% over the forecast period. Europe has several resources to establish itself as a significant player in technologies related to machine learning. Top-class universities, a strong consumer market, and a combination of corporate giants and startups in sectors transformed by machine intelligence, ranging from logistics and health care to finance and entertainment, are expected to impact the market positively. The region's adoption of advanced technologies like AI involving machine learning and deep learning is analyzed to boost the market growth. Europe has some of the largest global pharmaceutical companies and a growing number of AI healthcare startups working on drug development to hospital workforce logistics. More often, AI and ML are deployed to function together, increasing the need for MLaaS to train various data models collected by AI and automate the process in health care. For instance, Merantix, Germany-based AI research and incubator lab, is developing a cloud-based, on-demand platform that may put its cancer-detection AI at the disposal of radiologists globally.

Asia-Pacific is one of the most important markets for cloud and ML technology. The growing cloud and ML adoption among regional SMEs and increasing investments by all the end-users in ML technology in countries such as India, China, and South Korea, are a few of the major factors driving the market for ML as a service in the region. Since the cloud is the region's dominant industry vertical, MLaaS adoption has been significant, with most businesses using the cloud to go digital. The region is witnessing a significant increase in the adoption of ML and other cutting-edge technologies in the public and private sectors as the cloud and web API market develops.

The Rest of the world covers regions like Latin America and the Middle East and Africa. The region's challenges include adapting business processes to use ML insights, a talent shortage, and data quality or availability. This region's universities witnessed a limited adoption of ML and cloud technology. A disconnect between universities and industry is not resulting in a healthy startup environment in the region related to MLaaS. However, the region is witnessing gradual growth with investments. The region is witnessing various investments to raise funds for research and development in the market. For instance, in August 2020, CyberLabs partnered with Redpoint Eventures to raise USD 5 million to develop AI and deploy machine learning-as-a-service.

Segmental Analysis

The global machine learning as a service (MLaaS) market is bifurcated into application, organization size, and end-user.

Based on application, the global MLaaS market is bifurcated into marketing and advertisement, automated network management, predictive maintenance, fraud detection and risk analytics, and others.

The marketing and advertisement segment dominates the global market and is expected to exhibit a CAGR of 38.30% over the forecast period. Machine learning (ML) allows marketing companies to make quick, critical decisions based on big data. In addition, ML assists marketing enterprises in responding faster to the changes in the quality of traffic brought about by advertisement campaigns. According to a recent survey conducted by Dun and Bradstreet, 90% of the chief marketing officers in Indian cities plan on adopting marketing automation tools by the end of 2021. Furthermore, three of the four surveyed CMOs already use AI and ML tools to manage their database to automate the processes and improve their decision-making and customer interaction. The adoption of ML tools to manage databases and the move toward marketing automation are leading to the market's growth for MLaaS.

The fraud detection and risk analysis application of machine learning-as-a-service is proliferating due to the rapid increase in fraudulent activities, heavy vendor investments, and other factors. A survey from the Association of Certified Fraud Examiners (ACFE) claimed that the average cost of fraud to a company is greater than USD 1.5 million. Since the beginning of the pandemic, fraud risks have increased across all companies in every industry, and according to the survey conducted by ACFE, 79% of the respondents said that they had seen a rise in the overall level of fraud compared to 77% of the respondents in August and 68% in May. Increased fraudulent activities and remote working environments have led to ML-based services for tackling fraudulent activities.

Based on organization size, the global MLaaS market is bifurcated into large enterprises and small and medium enterprises.

The large enterprise segment owns the highest market share and is predicted to exhibit a CAGR of 38.34% over the forecast period. Large businesses are leveraging the power of Machine Learning methods to help them extract better quality information, increase productivity, reduce costs, and extract more value from their data. Large organizations are one of the major drivers in the growth of the MLaaS market. Service use is more likely to increase as these firms adopt deep learning and supervised and unsupervised machine learning technology for diverse applications. Large enterprises utilize these technological techniques for various reasons, including cost and risk.

According to the World Bank, SMEs are accountable for most businesses worldwide and vital contributors to job creation and global economic development. SMEs represent about 90% of the total businesses and more than 50% of the total employment worldwide. According to the Asia-Pacific Economic Corporation, SMEs account for over 97% of all businesses across the APEC economies. About 34% of SMEs in the Middle East and North Africa belong to the GCC region. Due to such factors, the market for MLaaS has a potential scope during the forecast period.

Based on end-user, the global MLaaS market is divided into IT and telecom, automotive, healthcare, aerospace and defense, retail, government, BFSI, and others.

The BFSI segment is the most significant contributor to the market and is anticipated to exhibit a CAGR of 37.19% over the forecast period. The BFSI sector has utilized AI and machine technologies more frequently in recent years to boost operational effectiveness and enhance the customer experience. The demand for machine learning BFSI applications increases as data receive more attention. Massive amounts of data, reasonably priced computing power, and cost-effective storage may enable machine learning to generate quick and reliable results. Additionally, the machine learning-driven approach to system modernization is anticipated to enable collaboration between enterprises and other fintech services to adapt to contemporary demands and regulations while enhancing safety and enabling security.

Machine Learning has played an essential role in aerospace and defense applications. There has been a demand for ML in automated systems, including space robotics and crewless aerial vehicles. Various technological opportunities have arisen, requiring unique approaches and algorithms to address corresponding technical challenges. Some other applications where Machine Learning is widely used include optimization algorithms in structural engineering to design fail-safe aerospace structures and solve problems, dealing with uncertainties in structural properties, unsteady aerodynamic loading, and flow/flight control system parameters.

Recent Developments

• May 2023- MakeMyTrip and Microsoft collaborated to redesign the website using generative AI. Making voice-assisted booking available in Indian languages is part of a joint initiative by these firms to increase accessibility and inclusivity in trip planning.

• May 2023- Bharti Airtel established an artificial intelligence (AI)/machine learning-based solution to actively detect, prevent, and eradicate phishing, spam, and messaging-based fraud, which it is testing in collaboration with HDFC Bank.