Insights into the global machine learning as a service market through 2028

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Machine learning as a global services market

Machine learning as a global services market

Machine learning as a global services market

Dublin, Nov. 24, 2022 (GLOBE NEWSWIRE) — The “Global Machine Learning as a Service Market Size Analysis Report, Share and Industry Trends by End User, by Offering, by Organization Size, by Application, by Outlook and regional forecasts, 2022-2028″ report has been added to ResearchAndMarkets.com offer.

The global market size of Machine Learning as a Service is projected to reach USD 36.2 billion by 2028, with a market growth of 31.6% CAGR during the forecast period.

Machine learning is a data analysis method that involves analyzing statistical data to create the desired prediction output without the use of explicit programming. It uses a sequence of algorithms to understand the link between datasets in order to produce 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.

Increasing demand for cloud computing, as well as growth related to artificial intelligence and cognitive computing, are major machine learning drivers for growth in the service sector. The growing demand for cloud-based solutions, such as cloud computing, the increase in the adoption of analytical solutions, the growth of the artificial intelligence and cognitive computing market, the increase in application areas and the shortage of professionals are all impacting the machine learning as a service market.

As more and more businesses migrate their data from on-premises storage to cloud storage, the need for efficient data organization grows. Since MLaaS platforms are essentially cloud service 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 data.

For organizations, MLaaS providers offer features like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, credit ratings, business intelligence, and healthcare, among other things. The actual calculations of these processes are pulled from the MLaaS providers, so data scientists don’t have to worry about them. For machine learning experimentation and model building, some MLaaS providers even have a drag-and-drop interface.

COVID-19 impact analysis

The COVID-19 pandemic has had a substantial impact on the health, economic and social systems of many countries. It has claimed millions of lives around the world and left the economic and financial systems in tatters. Individuals can benefit from knowledge of individual-level susceptibility variables to better understand and address their psychological, emotional, and social well-being.

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

Market growth factors

Increased demand for cloud computing and Big Data boom

The industry is growing due to the increased acceptance 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, giving you the advantage of evaluating the data collected in real time on the cloud.

Cloud computing enables data analysis from anywhere, anytime. Additionally, using the cloud to implement machine learning allows companies to obtain actionable data, such as consumer behavior and purchasing trends, from virtually connected data warehouses, reducing infrastructure and storage costs. As a result, machine learning as a service activity is growing as cloud computing technology is more widely adopted.

Using machine learning to power artificial intelligence systems

Machine learning is used to power 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 current endeavors such as big data infrastructure and cloud computing.

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

Market restrictive factors

Technical restrictions and inaccuracies of ML

The ML platform offers a myriad of benefits that help in market expansion. However, several parameters on the platform are expected to impede market expansion. The presence of inaccuracies in these algorithms, sometimes immature and underdeveloped, is one of the main constraints of the market.

In the manufacturing industries of big data and machine learning, accuracy is key. A minor flaw in the algorithm could result in the production of incorrect items. This should exorbitantly increase the operating costs for the production unit owner rather than reduce them.

Ratio attribute

Details

no. or pages

337

Forecast period

2021 – 2028

Estimated market value (USD) in 2021

5515 million dollars

Projected market value (USD) by 2028

$36204 million

Compound annual growth rate

31.6%

Region covered

Global

Main topics covered:

Chapter 1. Market Scope and Methodology

Chapter 2. Market overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market composition and scenario
2.2 Key factors affecting the market
2.2.1 Market drivers
2.2.2 Market Restrictions

Chapter 3. Competitor Analysis – Global
Cardinal matrix 3.1 KBV
3.2 Recent industry-wide strategic developments
3.2.1 Partnerships, collaborations and agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisitions and Mergers
3.3 Market Share Analysis, 2021
3.4 Main winning strategies
3.4.1 Major Leading Strategies: Percentage Distribution (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) Major Players

Chapter 4. Global Machine Learning as a Service Market by End User
4.1 Global IT and Telecom Market by Regions
4.2 Global BFI Market by Regions
4.3 Global Manufacturing Market by Regions
4.4 Global retail market by region
4.5 Global Healthcare Healthcare Market by Regions
4.6 Global Energy and Utilities Market by Regions
4.7 Global Public Sector Market by Region
4.8 Global Aerospace and Defense Market by Regions
4.9 Global Other End Users Market by Regions

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

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

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 Machine Vision Market by Regions
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 Regions
7.7 Global Augmented and Virtual Reality Market by Regions
7.8 Global Others Market by Regions

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

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 Recent strategies and developments:
9.1.5.1 Product launches and product expansions:
9.1.5.2 Acquisitions and mergers:
9.2 Oracle Company
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 Inc
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 Recent strategies and developments:
9.3.5.1 Partnerships, collaborations and agreements:
9.3.5.2 Product launches and product expansions:
9.4 Amazon Web Services, Inc. (Amazon.com, Inc.)
9.4.1 Company overview
9.4.2 Financial analysis
9.4.3 Segment analysis
9.4.4 Recent strategies and developments:
9.4.4.1 Partnerships, collaborations and agreements:
9.4.4.2 Product launches and product expansions:
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 Recent strategies and developments:
9.5.5.1 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 Recent strategies and developments:
9.6.5.1 Partnerships, collaborations and agreements:
9.6.5.2 Product launches and product expansions:
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 Recent strategies and developments:
9.8.2.1 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 about this report, please visit https://www.researchandmarkets.com/r/f69w74

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