The Market Statsville Group (MSG) publishes the new report on the "Machine Learning-as-a-Service Market by Application (Marketing and Advertisement, Predictive Maintenance, Automated Network Management, Fraud Detection, and Risk Analytics, and Other Applications (NLP, Sentiment Analysis, and Computer Vision)), by Organization Size (Small and Medium Enterprises and Large Enterprises), by End-Users (IT and Telecom, Automotive, Healthcare, Aerospace and Defense, Retail, Government, BFSI, and Other End Users (Education, Media and Entertainment, Agriculture, and Trading Market Place)), by Region – Global Share and Forecast to 2030". The global machine learning-as-a-service market size is projected to grow at a CAGR of 39.8% to reach USD 32.0 billion by 2030 from USD 2.2 billion in 2021. The main benefit of MLaaS is that customers can get started with ML applications quickly without the need to build in-house infrastructure or invest heavily in storage systems, servers, and software licenses. However, MLaaS platforms have certain downsides that keep some companies from using them. For instance, MLaaS solutions might not fit the company's specific needs. The prices and types of MLaaS services fall in line with the variations in all cloud computing services. The user needs to pay different costs per minute per processor, GPUs, GBs stored, and region. Also, the region will determine available resources, latency speeds, accessibility, and market prices.

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Furthermore, financial fraud is expected to be one of the significant areas of application for MLaaS. As payments become more flexible with the adoption of mobile and internet banking services, financial institutions are increasingly using machine-learning-based fraud detection systems to secure their online and mobile platforms. Hence, fraud detection is expected to become one of the core spaces for machine learning applications.

Global Machine Learning-as-a-Service Market Segmentation

The machine learning-as-a-service market has been segmented into the application, organization size, end-users, and region.

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

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By Region Outlook (Sales, USD Million, 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)

Large Enterprises, by organization size, is estimated to hold the largest market share during the forecast period

Based on method, the global machine learning-as-a-service market is segregated into small and medium enterprises and large enterprises. The large enterprise segment generated the highest revenue of 61.5% of the total global machine learning-as-a-service market in 2021, and it is expected to register a significant CAGR from 2022 to 2030. 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. As these organizations were increasingly adopting deep learning and supervised and unsupervised Machine Learning technologies for various applications, service adoption is more likely to increase. The cost and risk are some of the major factors driving the adoption of these technologies among large organizations.

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North America accounts for the largest market share in machine learning-as-a-service market 2021

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. In 2021, North America accounted for the highest market share of 44.8% in the global machine learning-as-a-service market and was anticipated to maintain its dominance throughout the forecast period. The United States has a strong innovation ecosystem fueled by strategic federal investments in advanced technology, visionary scientists and entrepreneurs from all over the world, and world-class research institutions, which has aided the growth of MLaaS in the North American region. In addition, the region is seeing a significant increase in 5G, IoT, and connected devices. As a result, through virtualization, network slicing, new use-cases, and service requirements, Communications Service Providers (CSPs) must effectively manage an ever-increasing complexity. As traditional network and service management approaches are no longer viable, this is expected to drive MLaaS solutions.

Furthermore, major technology companies in the region, such as Microsoft, Google, Amazon, and IBM, have entered the ML as a Service race as major players. Because each company has a large public cloud infrastructure and machine learning platforms, they can offer ML as a Service to those looking to use AI for everything from customer service to robotic process automation, marketing, analytics, predictive maintenance, and more. They can also help train the AI data models that are being deployed.

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This research report includes an examination of the leading companies' development and marketing strategies and an examination of their product portfolios and winning strategies in the machine learning-as-a-service market. It also consists of the profiles of leading companies such as Microsoft Corporation, Fair Isaac Corporation (FICO), Google LLC, IBM Corporation, Hewlett Packard Enterprise Company, Yottamine Analytics LLC, BigML Inc., SAS Institute Inc., Iflowsoft Solutions Inc., Amazon Web Services Inc., Monkeylearn Inc., Sift Science Inc., and H2O.ai Inc.

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