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

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Global Machine Learning as a Service Market

Global Machine Learning as a Service Market

Global Machine Learning as a Service Market

Dublin, Nov. 24, 2022 (GLOBE NEWSWIRE) — The Global Machine Learning as a Service Market Size, Share & Industry Trends Analysis Report by End User, by Offering, by Organization Size, by Application, by Regional Outlook and Forecast report , 2022 – 2028″ report was added Offer.

The size of the global machine learning as a service market is expected to reach US$36.2 billion by 2028, representing a market growth of CAGR 31.6% during the forecast period.

Machine learning is a data analysis method that involves statistical data analysis to create the desired predictive output without the use of explicit programming. It uses a suite of algorithms to understand the linkage between datasets to achieve the desired result. It is designed to include artificial intelligence (AI) and cognitive computing capabilities. Machine Learning as a Service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing as well as growth related to artificial intelligence and cognitive computing are key growth drivers of machine learning in the service industry. The growing demand for cloud-based solutions like cloud computing, increasing adoption of analytical solutions, growth of artificial intelligence and cognitive computing market, increasing application areas and shortage of trained professionals are affecting the machine learning as a service market.

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

For businesses, MLaaS providers offer features such as data visualization and predictive analytics. They also offer APIs for sentiment analysis, facial recognition, credit scores, business intelligence, and healthcare, among others. The actual calculations 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.

COVID-19 Impact Analysis

The COVID-19 pandemic is having a significant impact on the health, economic and social systems of many countries. It has claimed millions of lives around the world and wrecked economic and financial systems. Individuals can benefit from knowledge of vulnerability variables at an individual level to better understand and manage 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 tracked in multiple countries using population surveillance approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, are tracking coronavirus cases using surveillance camera footage and geolocation data.

market growth factors

Increased demand for cloud computing and boom in big data

The industry is growing due to the increasing acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely adopted by all companies offering enterprise storage solutions. The data analysis takes place online via cloud storage, which offers the advantage that the data collected in the cloud is evaluated in real time.

Cloud computing enables data analysis from anywhere and at any time. Additionally, leveraging the cloud to deliver machine learning enables businesses to obtain useful data such as consumer behavior and purchasing trends virtually from linked data warehouses, reducing infrastructure and storage costs. As a result, the business of machine learning as a service is growing as cloud computing technology becomes more widespread.

Using machine learning to fuel artificial intelligence systems

Machine learning is used to drive reasoning, learning, and self-correction in artificial intelligence (AI) systems. Expert systems, speech recognition and computer vision are examples of AI applications. The rise in popularity of AI is due to recent efforts such as big data infrastructure and cloud computing.

Top companies from all industries including Google, Microsoft and Amazon (Software & 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 top firms have also funded young startups to develop new creative technologies.

Market Inhibiting Factors

Technical Limitations and Inaccuracies of ML

The ML platform offers a wealth of benefits that help in market expansion. However, several parameters on the platform are predicted to hamper the market expansion. The presence of inaccuracies in these algorithms, sometimes immature and underdeveloped, is one of the main constraining factors of the market.

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In the big data and machine learning manufacturing industry, precision is key. A small error in the algorithm could result in the wrong items being produced. This is expected to exorbitantly increase operating costs for the production unit owner, rather than reduce them.

report attribute


number of pages


forecast period

2021 – 2028

Estimated market value (USD) in 2021

5515 million dollars

Projected market value (USD) by 2028

36204 million dollars

Annual growth rate


Regions Covered


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 Key factors affecting the market
2.2.1 Market Drivers
2.2.2 Market Restrictions

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 Enhancements
3.2.3 Acquisition and Mergers
3.3 Market Share Analysis, 2021
3.4 Top Winning Strategies
3.4.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.4.2 Key strategic moves: (Product launches and product enhancements: Jan 2018 – May 2022) Leading players
3.4.3 Key Strategic Step: (Partnership, Cooperation and Agreement: 2019 April – 2022 March) Leading Actors

Chapter 4. Global Machine Learning as a Service Market by End Users
4.1 Global IT & Telecom Market by Regions
4.2 Global BFSI Market by Regions
4.3 Global Manufacturing Market by Regions
4.4 Global Retail Market by Regions
4.5 Global Healthcare Market by Regions
4.6 Global Energy and Utilities Market by Regions
4.7 Global Public Sector Market by Regions
4.8 Global Aerospace & Defense Market by Regions
4.9 Global Other End-Users Market by Regions

Chapter 5. Global Machine Learning as a Service Market by Offering
5.1 Global Services-Only Market by Regions
5.2 Global Solution (Software Tools) Market by Regions

Chapter 6. Global Machine Learning as a Service Market, by Company Size
6.1 Global Large Enterprises Market by Regions
6.2 Global Small and Medium Enterprises Market by Regions

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

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

Chapter 9. Company Profiles
9.1 Hewlett Packard Enterprise Company
9.1.1 Corporate Overview
9.1.2 Financial Analysis
9.1.3 Segment and Regional Analysis
9.1.4 Research and Development Costs
9.1.5 Recent policies and developments: Product launches and product enhancements: Acquisitions and Mergers:
9.2 Oracle Corporation
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segment and Regional Analysis
9.2.4 Research and Development Costs
9.2.5 SWOT Analysis
9.3 Google LLC
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Segment and Regional Analysis
9.3.4 Research and Development Costs
9.3.5 Recent policies and developments: Partnerships, collaborations and agreements: Product launches and product enhancements:
9.4 Amazon Web Services, Inc. (, Inc.)
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Segment Analysis
9.4.4 Recent policies and developments: Partnerships, collaborations and agreements: Product launches and product enhancements:
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 Costs
9.5.5 Recent policies and developments: Partnerships, collaborations and agreements:
9.6 Microsoft Corporation
9.6.1 Company Overview
9.6.2 Financial Analysis
9.6.3 Segment and Regional Analysis
9.6.4 Research and Development Costs
9.6.5 Recent policies and developments: Partnerships, collaborations and agreements: Product launches and product enhancements:
9.7 Fair Isaac Corporation (FICO)
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segment and Regional Analysis
9.7.4 Research and Development Costs
9.8 SAS Institute, Inc.
9.8.1 Company Overview
9.8.2 Recent policies and developments: Partnerships, collaborations and agreements:
9.9 Yottamine Analytics, LLC
9.9.1 Company Overview
9.10. BigML
9.10.1 Company Overview

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