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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, 2022 – 2028” report added ResearchAndMarkets.coms offer.

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

Machine learning is a method of data analysis that involves statistical data analysis to create the desired prediction output without the use of explicit programming. It uses a series of algorithms to understand the link between data sets to produce the desired result. It is designed to incorporate artificial intelligence (AI) and cognitive computing functionalities. 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 associated with artificial intelligence and cognitive computing, are key machine learning drivers of growth for the services industry. The growth in the demand for cloud-based solutions such as cloud computing, the increase in the use of analytic solutions, the growth of the artificial intelligence and cognitive computing market, more application areas and a scarcity of trained professionals are all impacting machine learning as a service market.

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

For organizations, MLaaS providers provide capabilities such as data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, credit ratings, business information, and healthcare, among others. The actual calculations of these processes are abstracted by MLaaS providers, so data scientists don’t have to worry about it. For machine learning experimentation and model construction, some MLaaS providers even have a drag and drop interface.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a significant impact on the health, economic and social systems of many countries. It has resulted in millions of deaths around the world and left economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level sensitivity variables 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 tracked and traced across countries using population monitoring approaches enabled 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 an explosion of big data

The industry is growing due to the increased adoption of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies providing enterprise storage solutions. Data analysis is performed online using cloud storage, which offers 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 enables companies to pull useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, reducing infrastructure and storage costs. As a result, the machine learning-as-a-service business is growing as cloud computing technology becomes more widely adopted.

Using machine learning to fuel artificial intelligence systems

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

Top companies in various 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 a key strategic driver and have begun investing in machine learning to develop more advanced systems. These top firms have also provided financial support to young start-ups to produce new creative technology.

Market Limiting Factors

Technical limitations and inaccuracies of ML

The ML platform offers a plethora of benefits that aid in market expansion. However, various parameters on the platform are expected to hinder the expansion of the market. The presence of inaccuracies in these algorithms, which are sometimes immature and underdeveloped, is one of the major limiting factors of the market.

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In the big data and machine learning manufacturing industries, precision is critical. A small error in the algorithm can cause incorrect items to be produced. This is expected to exorbitantly increase rather than decrease operational costs for the owner of the production unit.

Report attribute


Number of pages


Forecast period

2021 – 2028

Estimated market value (USD) in 2021

$5515 million

Expected market value (USD) by 2028

$36204 million

Compound annual growth rate


Regions Covered


Main topics:

Chapter 1. Market reach 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 strategic developments across the industry
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Enhancements
3.2.3 Acquisitions and Mergers
3.3 Market Share Analysis, 2021
3.4 Best Winning Strategies
3.4.1 Key Leading Strategies: Percentage Breakdown (2018-2022)
3.4.2 Key Strategic Move: (Product Launches and Product Expansions: 2018, Jan – 2022, May) Key Players
3.4.3 Key Strategic Move: (Partnership, Collaboration and Agreement: 2019, April – 2022, March) Leading Players

Chapter 4. Global Machine Learning as a Service Market by End User
4.1 Global IT and Telecom Market by Region
4.2 Global BFSI Market by Region
4.3 Global Production Market by Region
4.4 Global Retail Market by Region
4.5 Global Healthcare Market by Region
4.6 Global Energy & 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. Global Machine Learning as a Service Market by Offerings
5.1 Global Services Only Market by Region
5.2 Global Solution (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 Small Business Market by Region

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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 Others Market by Region

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

Chapter 9. Company Profiles
9.1 Hewlett-Packard Enterprise Company
9.1.1 Business Overview
9.1.2 Financial Analysis
9.1.3 Segmental and Regional Analysis
9.1.4 Research and Development Expenses
9.1.5 Recent strategies and developments: Product Launches and Product Enhancements: Acquisitions and Mergers:
9.2 Oracle Corporation
9.2.1 Business 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 Business Overview
9.3.2 Financial Analysis
9.3.3 Segmental and Regional Analysis
9.3.4 Research and Development Expenses
9.3.5 Recent strategies and developments: Partnerships, Collaborations and Agreements: Product Launches and Product Enhancements:
9.4 Amazon Web Services, Inc. (Amazon.com, Inc.)
9.4.1 Business Overview
9.4.2 Financial Analysis
9.4.3 Segment Analysis
9.4.4 Recent strategies and developments: Partnerships, Collaborations and Agreements: Product Launches and Product Enhancements:
9.5IBM Corporation
9.5.1 Business Overview
9.5.2 Financial Analysis
9.5.3 Regional and Segment Analysis
9.5.4 Research and Development Expenses
9.5.5 Recent strategies and developments: Partnerships, Collaborations and Agreements:
9.6 Microsoft Corporation
9.6.1 Business Overview
9.6.2 Financial Analysis
9.6.3 Segmental and Regional Analysis
9.6.4 Research and Development Expenses
9.6.5 Recent strategies and developments: Partnerships, Collaborations and Agreements: Product Launches and Product Enhancements:
9.7 Fair Isaac Corporation (FICO)
9.7.1 Business 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 Business Overview
9.8.2 Recent strategies and developments: Partnerships, Collaborations and Agreements:
9.9 Yottamine Analytics, LLC
9.9.1 Business Overview
9.10. BigML
9.10.1 Business Overview

For more information on this report visit https://www.researchandmarkets.com/r/f69w74


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