Description

The market size of the machine learning-as-a-service (MLaaS) was USD 2.2 billion in 2021. It is estimated to reach USD 32.0 billion by 2030, registering a CAGR of 39.8% from 2022-2030. With advancements in data science and artificial intelligence, the performance of machine learning accelerated at a rapid pace. Companies are beginning to recognize the potential of this technology, and as a result, adoption rates are expected to rise over the forecast period. Machine learning solutions are available on a subscription basis, making it easier for consumers to access this technology.

Furthermore, it offers pay-as-you-go flexibility. Microservices offered by major cloud computing firms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform are examples of MLaaS products. Natural language processing, computer vision, and general machine learning algorithms are commonly included in these solutions.

Moreover, the continuous evolution of these service offerings has made them cost-effective and expanded their application across multiple end-user industries. AWS has continually added new capabilities to Amazon SageMaker since its launch. The added features included Amazon SageMaker Ground Truth which helps developers build highly accurate annotated training datasets. The company also added SageMaker RL, which helps professionals use a powerful reinforcement learning technique.

Global Machine Learning-as-a-Service Market Definition

Machine learning as a service is an array of services that provide machine learning tools as part of cloud computing services. MLaaS helps the clients benefit from machine learning without the cognate time, cost, and risk of establishing an in-house internal machine learning team. Infrastructural concerns such as data pre-processing, model evaluation, model training, and ultimately, predictions can be mitigated through MLaaS.

Machine learning, a subfield of artificial intelligence in its most straightforward description, spans a broad set of algorithms used to extract valuable models from raw data and grew out of traditional statistics and analysis.

COVID-19 Impact on the Global Machine Learning-as-a-Service Market

Machine learning has significantly helped in analyzing data related to COVID-19. In April 2020, Amazon Web Services launched Cord-19 Search, a new website powered by ML that could help researchers quickly and easily utilize natural language questions to search tens of thousands of research papers and documents. In addition, in October 2020, Amazon open-sourced a toolset for data scientists and researchers to better model and understand coronavirus progression in each community over time. This toolset has a disease progression simulator and multiple ML models to test the impact of various interventions.

For instance, several researchers use machine learning to create a smart monitoring system that tracks and detects the suspected COVID-19 infected persons. One proposed system is a new framework integrating machine learning, cloud, fog, and Internet of Things (IoT) technologies to create a COVID-19 disease monitoring and prognosis system.

Global Machine Learning-as-a-Service Market Dynamics

Drivers: Increasing Adoption of IoT and Automation

IoT operations ensure that thousands or more devices on an enterprise network run correctly and safely and that the data collected is both timely and accurate. While sophisticated back-end analytics engines handle the heavy lifting of processing a stream of data, ensuring data quality is frequently left to antiquated methods. Some IoT platform vendors are incorporating machine learning technology to improve their operations management capabilities in order to maintain control over sprawling IoT infrastructures.

Machine learning could demystify the hidden patterns in IoT data by analyzing significant volumes of data utilizing sophisticated algorithms. Machine learning inference could replace manual processes with automated systems that use statistically derived actions in critical processes. The IoT data modeling process is automated with ML solutions, eliminating the time-consuming and labor-intensive model selection, coding, and validation activities.

Challenges: Privacy and Data Security Concerns

Machine learning as a service (MLaaS) leverages deep learning techniques for predictive analytics to enhance decision-making. However, the usage of MLaaS introduces security challenges for ML model owners and data privacy challenges for data owners. Data owners are concerned about the privacy and safety of their data on MLaaS platforms. In contrast, MLaaS platform owners worry that their models could be stolen by adversaries who pose as clients.

To engage in predictions, a model owner needs to receive data from the clients. However, the data may consist of sensitive information. Thus, most clients are reluctant to provide their data. Furthermore, there is an issue about the prediction result's privacy and whether it is safe from being accessed by unauthorized parties. In this scenario, privacy-preserving deep learning (PPDL) is needed to tackle the challenge. The future direction of PPDL will focus on combining federated learning and overcoming the current privacy issues during the data collection phase in MLaaS.

Segmentation of Global Machine Learning-as-a-Service Market

The study categorizes the machine learning-as-a-service market based on application, organization size, and end-users at the regional and global levels.

By Application Outlook (Sales/Revenue, USD Billion, 2017-2030)

• Marketing and Advertisement
• Predictive Maintenance
• Automated Network Management
• Fraud Detection and Risk Analytics
• Other Applications
  • NLP
  • Sentiment Analysis
  • Computer Vision

By Organization Size Outlook (Sales/Revenue, USD Billion, 2017-2030)

• Small and Medium Enterprises
• Large Enterprises

By End-Users Outlook (Sales/Revenue, USD Billion, 2017-2030)

• IT and Telecom
• Automotive
• Healthcare
• Aerospace and Defense
• Retail
• Government
• BFSI
• Other End Users
  • Education
  • Media and Entertainment
  • Agriculture
  • Trading Market Place

By Region Outlook (Sales/Revenue, USD Billion, 2017-2030)

• North America (US, Canada, Mexico)
• South America (Brazil, Argentina, Colombia, Peru, Rest of Latin America)
• Europe (Germany, Italy, France, UK, Spain, Poland, Russia, Slovenia, Slovakia, Hungary, Czech Republic, Belgium, the Netherlands, Norway, Sweden, Denmark, Rest of Europe)
• Asia Pacific (China, Japan, India, South Korea, Indonesia, Malaysia, Thailand, Vietnam, Myanmar, Cambodia, the Philippines, Singapore, Australia & New Zealand, Rest of Asia Pacific)
• The Middle East & Africa (Saudi Arabia, UAE, South Africa, Northern Africa, Rest of MEA)

The marketing and advertisement segment is projected to account for the largest market share by application

Based on application, the global machine learning-as-a-service market is divided into marketing and advertisement, automated network management, predictive maintenance, fraud detection and risk analytics, and other applications. In 2021, the marketing and advertisement segment accounted for the largest market share of 33.6% in the global machine learning-as-a-service market. Machine learning (ML) provides marketing companies with an opportunity 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.

Also, the current dynamic creative optimization (DCO) approach requires the brands to pre-plan the right message for the appropriate consumer context and provide little room for learned adaptation as the campaign matures. However, predictive and regressive machine learning models are reshaping the prospects of dynamic creative assembly by empowering the brands to predict which elements resonate best with every audience member.

Asia Pacific accounts for the highest CAGR during the forecast period

Based on the regions, the global machine learning-as-a-service market has been segmented across North America, Asia-Pacific, Europe, South America, and the Middle East & Africa. Globally, Asia Pacific is estimated to hold the highest CAGR of 41.7% in the global machine learning-as-a-service market during the forecast period. The Asia-Pacific region is one of the most significant cloud and ML technology markets. The growing cloud and ML adoption among regional SMEs and increasing investments by all the end-users in ML technology are major factors driving the market for ML as a service in the region.

Further, the market's growth in terms of robotic process automation, machine-to-machine communication, cloud manufacturing, and cloud AI may directly create the need for ML as a service, as ML is the major functioning factor to automate various tasks and support predictions for these markets. Emerging countries, like India and Taiwan, are heavily investing toward adopting new ML-based services or models, further expanding the market studied’s application scope. The growing investments by multiple startups and venture capitals (VC) in the region act as a catalyst in bringing innovation into the market.

Key Market Players

The machine learning-as-a-service market is mildly concentrated in nature with few numbers of global players operating in the market such as Microsoft Corporation, SAS Institute Inc., Fair Isaac Corporation (FICO), Google LLC, IBM Corporation, Hewlett Packard Enterprise Company, Yottamine Analytics LLC, BigML Inc., Iflowsoft Solutions Inc., Amazon Web Services Inc., Monkeylearn Inc., Sift Science Inc., and H2O.ai Inc. Every company follows its business strategy to attain the maximum market share.

Recent Developments

• In June 2021, Hyundai Motor Company heavily invested human and material resources in the race to develop self-driving cars. The scalable AWS Cloud and Amazon SageMaker, including the new SageMaker library for data parallelism, helped Hyundai Motor Company significantly speed up model training.
• In April 2021, Microsoft announced an open dataset for transportation, health, genomics, labor and economics, population, and safety, and supplemental and common datasets to improve the accuracy of machine learning models using publicly available datasets. This also enables the company to deliver hyper-scale insights by combining Azure Open Datasets with Azure's machine learning and data analytics solutions, boosting MLaaS sales.
• In June 2020, Eurobank, one of the four Greek systemic banks, expanded its use of FICO compliance solutions to cover all customer journey stages across multiple channels in response to new European regulations. For many years, Eurobank SA, part of the Eurobank Group, which operates in six countries, relied on FICO Siron Anti-Financial Crime Solutions.

Key Issues Addressed

• What is the market size by various segmentation of the machine learning-as-a-service by region and its respective countries?
• What are the customer buying behavior, key takeaways, and Porter's five forces of the machine learning-as-a-service market?
• What are the key opportunities and trends for manufacturers involved in the machine learning-as-a-service supply chain?
• What are the market's fundamental dynamics (drivers, restraints, opportunities, and challenges)?
• What and how are regulations, schemes, patents, and policies impacting the market's growth?
• What are the upcoming technological solutions influencing market trends? How will existing companies adapt to the new change in technology?
• The market player positioning, top winning strategies by years, company product developments, and launches will be?
• How has COVID-19 impacted the global market's demand and sales of machine learning-as-a-service? Also, the expected BPS drop or rise count of the market and market predicted recovery period.
• Detailed analysis of the competitors and their latest launch, and what are the prominent startups introduced in the target market? Also, detailed company profiling of 25+ leading and prominent companies in the market.