Insights on the Machine Learning as a Service Global Market to 2028

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Global Machine Learning as a Service Marketplace

Global Machine Learning as a Service Marketplace

Global Machine Learning as a Service Marketplace

Dublin, Nov. 24, 2022 (GLOBE NEWSWIRE) — “Global Machine Learning as a Service Market Size, Share and Industry Trend Analysis Report by End User, by Supply, by Organization Size, by Application, by Regional Outlook and Forecast, 2022 – 2028” report is attached the offer.

The global machine learning as a service market size is expected to reach USD 36.2 billion by 2028, growing at a CAGR of 31.6% during the forecast period.

Machine learning is a data analysis technique that involves statistical analysis of data to produce a desired predictive result without the use of explicit programming. It uses a sequence of algorithms to understand the relationship between data sets to produce the desired result. It is designed to incorporate artificial intelligence (AI) and cognitive computing features. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, along with the growth associated with artificial intelligence and cognitive computing, are the main drivers for the growth of the machine learning as a service industry. Growing demand for cloud-based solutions such as cloud computing, growth in the adoption of analytical solutions, growth in the market for artificial intelligence and cognitive computing, broader application areas, and a shortage of trained professionals are influencing machine learning as a service. the market.

As more and more companies migrate their data from on-premises storage to cloud storage, the need for efficient data organization grows. Because MLaaS platforms are essentially cloud service providers, they provide solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier for data engineers to access and process data.

For organizations, MLaaS providers offer capabilities such as data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, credit scoring, corporate information, and healthcare, among others. The actual computations of these processes are abstracted by MLaaS providers, so data scientists don’t have to worry about them. For machine learning experiments and model building, some MLaaS providers even offer a drag-and-drop interface.

Analysis of the impact of Covid-19

The Covid-19 pandemic has significantly affected the health, economic and social systems of many countries. It has caused millions of deaths worldwide and left economies and financial systems reeling. Individuals can benefit from knowledge of individual-level vulnerability variables to better understand and cope with their psychological, emotional, and social well-being.

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Artificial intelligence technology is likely to help in the fight against the COVID-19 pandemic. Covid-19 cases are being tracked and traced across multiple countries using population surveillance approaches powered by machine learning and artificial intelligence. For example, researchers in South Korea are tracking coronavirus cases using surveillance camera footage and geolocation data.

Market growth factors

Increased demand for cloud computing and the rise of big data

The industry is growing due to the increasing adoption of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that provide enterprise storage solutions. Data analysis is performed online using cloud storage, providing the advantage of evaluating real-time data collected in the cloud.

Cloud computing enables data analysis from anywhere at any time. In addition, using the cloud to deploy machine learning allows companies to extract useful data, such as consumer behavior and buying trends, virtually from connected data warehouses, reducing infrastructure and storage costs. As a result, machine learning as a service business is growing due to the increasing adoption of cloud computing technology.

Using machine learning to power artificial intelligence systems

Machine learning is used to facilitate reasoning, learning and self-correction in artificial intelligence (AI) systems. Examples of AI applications include expert systems, speech recognition, and machine vision. The rise in popularity of AI is due to current efforts such as big data infrastructure and cloud computing.

Top companies across industries including Google, Microsoft and Amazon (software and IT); Bloomberg, American Express (financial services); and Tesla and Ford (Automotive) have identified AI and cognitive computing as key strategic drivers and have begun investing in machine learning to develop more advanced systems. These leading companies have also provided financial support to young start-ups to create new creative technologies.

Market restraints

ML technical limitations and inaccuracies

The ML platform provides many advantages that help expand the market. However, several parameters of the platform are predicted to hinder the market expansion. Inaccuracies in these algorithms, which are sometimes immature and underdeveloped, are one of the main limiting factors in the markets.

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In manufacturing industries with big data and machine learning, accuracy is critical. A small error in the algorithm can cause incorrect items to be crafted. This is expected to significantly increase production costs rather than reduce them.

Report attribute

more details

Number of pages


Forecast period

2021-2028 year

Estimated market value (USD) in 2021

5515 million USD

Projected market value (USD) by 2028

36204 million USD

Compound annual growth rate


Regions included


Main topics covered:

Chapter 1. Market scope and methodology

Chapter 2. Market overview
2.1 Introduction
2.1.1. Overview Market composition and scenario
2.2. The main factors affecting the market
2.2.1. Market drivers
2.2.2. Market constraints

Chapter 3. Competitive analysis – global
3.1 KBV cardinal matrix
3.2. Recent industry-wide strategic developments
3.2.1. Partnerships, collaborations and agreements
3.2.2. Product Launches and Product Extensions
3.2.3. Takeovers and mergers
3.3. Market share analysis, 2021
3.4. The most popular winning strategies
3.4.1. Key Leading Strategies: Percentage Distribution (2018-2022)
3.4.2. Key Strategic Step: (Product Launch & Product Expansion: Jan 2018-May 2022) Leading Players
3.4.3. Key Strategic Step: (Partnership, Collaboration and Agreement: April 2019-March 2022) Leading Players

Chapter 4. Global machine learning as an end-user service market
4.1. Global IT and Telecom Market by Region
4.2 Global BFSI Market by Region
4.3 Global manufacturing market by region
4.4 Global Retail Market by Region
4.5 Global Healthcare Market by Region
4.6. Global Energy and Utilities Market by Region
4.7 Global Public Sector Market by Region
4.8. Global aerospace and defense market by region
4.9 Global Other End User Market by Region

Chapter 5. The global machine learning as a service marketplace, offering
5.1. Only global services market by region
5.2 Global Solutions (Software Tools) Market by Region

Chapter 6. Global Machine Learning as a Service Market by Organization Size
6.1. Global Large Enterprise Market by Region
6.2. Global SMB market by region

Chapter 7. Global Machine Learning as a Service Market by Application
7.1. Global Marketing and Advertising Market by Region
7.2. Global Fraud Detection and Risk Management Market by Region
7.3. Global Computer Vision Market by Region
7.4 Global Security and Surveillance Market by Region
7.5. Global Predictive Analytics Market by Region
7.6. Global Natural Language Processing Market by Region
7.7. Global Augmented and Virtual Reality Market by Region
7.8. Global Other Commodity Market by Region

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Chapter 8. Global Machine Learning as a Service Market by Region

Chapter 9. Company profiles
9.1 Hewlett Packard Enterprise Company
9.1.1 Company overview
9.1.2. Financial analysis
9.1.3. Segmental and regional analysis
9.1.4. Research and development expenses
9.1.5. Latest strategies and achievements: Product Launches and Product Extensions: Acquisition and Merger:
9.2. Oracle Corporation
9.2.1 Company overview
9.2.2. Financial analysis
9.2.3. Segmental and regional analysis
9.2.4. Research and development expenses
9.2.5. SWOT analysis
9.3 Google LLC
9.3.1 Company overview
9.3.2. Financial analysis
9.3.3. Segmental and regional analysis
9.3.4. Research and development expenses
9.3.5. Latest strategies and achievements: Partnerships, collaborations and agreements: Product Launches and Product Extensions:
9.4 Amazon Web Services, Inc. (, Inc.)
9.4.1 Company overview
9.4.2. Financial analysis
9.4.3. Segmental analysis
9.4.4. Latest strategies and achievements: Partnerships, collaborations and agreements: Product Launches and Product Extensions:
9.5 IBM Corporation
9.5.1 Company overview
9.5.2. Financial analysis
9.5.3. Regional and segmental analysis
9.5.4. Research and development expenses
9.5.5. Latest strategies and achievements: Partnerships, collaborations and agreements:
9.6 Microsoft Corporation
9.6.1 Company overview
9.6.2. Financial analysis
9.6.3. Segmental and regional analysis
9.6.4 Research and development expenses
9.6.5. Latest strategies and achievements: Partnerships, collaborations and agreements: Product Launches and Product Extensions:
9.7. Fair Isaac Corporation (FICO)
9.7.1 Company overview
9.7.2. Financial analysis
9.7.3. Segmental and regional analysis
9.7.4. Research and development expenses
9.8 SAS Institute, Inc.
9.8.1 Company overview
9.8.2. Latest strategies and achievements: Partnerships, collaborations and agreements:
9.9 Yottamine Analytics, LLC
9.9.1 Company overview
9.10. BigML
9.10.1 Company overview

For more information on this report, visit


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