Machine Learning as a Service Market 2021 Key Trends, Applications and Future Developments to 2030 – SMU Daily Mustang

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

Plus, it offers pay-as-you-go flexibility. Microservices offered by major cloud computing companies 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 typically included in these solutions.

Moreover, the continuous evolution of these service offerings has made them cost-effective and extended their application to multiple end-user industries. AWS has continuously added new features to Amazon SageMaker since its launch. Features added included Amazon SageMaker Ground Truth, which helps developers create 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 a collection of services that provide machine learning tools as part of cloud computing services. MLaaS helps customers benefit from machine learning without the time, cost, and risk of building an in-house machine learning team. Infrastructure issues such as data preprocessing, model evaluation, model training, and ultimately forecasting can be alleviated with MLaaS.

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Machine learning, a subfield of artificial intelligence in its simplest description, covers a wide set of algorithms used to extract valuable patterns from raw data and grew out of traditional statistics and analytics. .

covid19 Global Machine Learning as a Service Market Impact

Machine learning has helped tremendously in analyzing data related to COVID-19. In April 2020, Amazon Web Services launched Cord-19 Search, a new ML-powered website that could help researchers quickly and easily use natural language questions to search tens of thousands of articles and research papers. . Additionally, in October 2020, Amazon made open source a set of tools for data scientists and researchers to better model and understand the progression of the coronavirus in each community over time. This toolset features a disease progression simulator and several ML models to test the impact of various interventions.

For example, several researchers are using machine learning to create an intelligent surveillance system that tracks and detects people suspected of being infected with COVID-19. One of the proposed systems is a novel framework integrating machine learning, cloud, fog, and Internet of Things (IoT) technologies to create a COVID-19 disease surveillance and prognosis system.

Global Machine Learning-as-a-Service Market Dynamics

Drivers: Increased adoption of IoT and automation

IoT operations ensure that thousands or more devices on a corporate network are functioning properly and securely and that the data collected is both accurate and timely. While sophisticated back-end analytics engines handle the heavy lifting of processing a stream of data, ensuring data quality is often left to antiquated methods. Some IoT platform vendors are integrating machine learning technology to improve their operations management capabilities to maintain control over sprawling IoT infrastructures.

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

Challenges: Privacy and Data Security Concerns

Machine Learning as a Service (MLaaS) leverages deep learning techniques for predictive analytics to improve decision making. However, using MLaaS presents security challenges for ML model owners and data privacy challenges for data owners. Data owners are concerned about the privacy and security of their data on MLaaS platforms. In contrast, owners of MLaaS platforms fear that their models will be stolen by adversaries posing as customers.

To engage in predictions, a model owner must receive data from customers. However, the data may contain sensitive information. Thus, most customers are reluctant to provide their data. Additionally, there is an issue regarding the confidentiality of the prediction result and whether it is safe to be accessed by unauthorized parties. In this scenario, privacy-preserving deep learning (PPDL) is needed to overcome the challenge. The future direction of PPDL will focus on combining federated learning and addressing current privacy issues during the data collection phase in MLaaS.

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Global Machine Learning as a Service Market Segmentation

The study categorizes the Machine Learning as a Service market based on application, organization size, and end users regionally and globally..

By Outlook app (Sales/Revenue, billion USD, 20172030)

  • Marketing and Advertising
  • Predictive maintenance
  • Automated Network Management
  • Fraud detection and risk analysis
  • Other Apps
    • NLP
    • Sentiment analysis
    • computer vision

By organization size (Sales/Revenue, billion USD, 20172030)

  • Small and medium enterprises
  • Large companies

From end-user perspectives (Sales/Revenue, billion USD, 20172030)

  • IT and Telecom
  • Automotive
  • Health care
  • Aeronautics and Defense
  • Retail
  • Government
  • BFSI
  • Other end users
    • Education
    • Media and entertainment
    • Agriculture
    • Trading Market Square

Outlook by region (Sales/Revenue, billion USD, 20172030)

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

The marketing and advertising segment expected to account for the largest market share by application

Based on the application, the global machine learning as a service market is divided into marketing and advertising, automated network management, predictive maintenance, fraud detection and risk analysis, and other applications.. In 2021, the marketing and advertising segment accounted for the largest market share of 33.6% in the Global Machine Learning-as-a-Service Market. Machine learning (ML) offers marketing companies the ability to make fast and critical decisions based on big data. Additionally, ML helps marketing companies respond more quickly to changes in traffic quality caused by advertising campaigns.

Additionally, the current Dynamic Creative Optimization (DCO) approach forces brands to pre-plan the right message for the appropriate consumption context and leave little room for learned adaptation as the campaign matures. . However, predictive and regressive machine learning models are reshaping dynamic creative stitching perspectives by allowing brands to predict which elements resonate best with each audience member.

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Asia Pacific represents the highest CAGR over the forecast period

On the basis of region, the global machine learning as a service market has been segmented into North America, AsiaPacific, Europe, South America, Middle East and 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 largest markets for cloud and ML technology. Growing adoption of cloud and ML among regional SMBs and rising investment by all end users in ML technology are major factors driving the ML-as-a-Service market in the region.

Also, the market growth in terms of robotic process automation, machine-to-machine communication, cloud manufacturing, and cloud AI can directly create the need for ML as a service, as ML is the main operating factor to automate various tasks and support predictions. for these markets. Emerging countries, such as India and Taiwan, are investing heavily in the adoption of new ML-based services or models, further expanding the scope of the market studied. The growing investments of several startups and venture capitalists (VC) in the region act as a catalyst to bring innovation to market.

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Main market players

The Machine Learning as a Service market is slightly concentrated in nature with few 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.. Each company follows its business strategy to achieve the maximum market share.

Sherry J. Basler