Insights on the Machine Learning as a Service Global Market to 2028 - Use of Machine Learning to Fuel Artificial Intelligence Systems is Driving Growth

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 has been added to ResearchAndMarkets.com's offering.

The Global Machine learning as a Service Market size is expected to reach $36.2 billion by 2028, rising at a market growth of 31.6% CAGR during the forecast period.

Machine learning is a data analysis method that includes statistical data analysis to create desired prediction output without the use of explicit programming. It uses a sequence of algorithms to comprehend the link between datasets in order to produce the desired result. It is designed to include 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 connected with artificial intelligence and cognitive computing, are major machine learning as service industry growth drivers. Growth in demand for cloud-based solutions, such as cloud computing, rise in adoption of analytical solutions, growth of the artificial intelligence & cognitive computing market, increased application areas, and a scarcity of trained professionals are all influencing the machine learning as a service market.

As more businesses migrate their data from on-premise storage to cloud storage, the necessity for efficient data organization grows. Since 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 organizations, MLaaS providers offer capabilities like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, creditworthiness evaluations, corporate intelligence, and healthcare, among other things. The actual computations of these processes are abstracted by MLaaS providers, so data scientists don't have to worry about them. For machine learning experimentation and model construction, some MLaaS providers even feature a drag-and-drop interface.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a substantial impact on numerous countries' health, economic, and social systems. It has resulted in millions of fatalities across the globe and has left the economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables in order to better understand and cope with their psychological, emotional, and social well-being.

Artificial intelligence technology is likely to aid in the fight against the COVID-19 pandemic. COVID-19 cases are being tracked and traced in several countries utilizing population monitoring approaches enabled by machine learning and artificial intelligence. Researchers in South Korea, for example, track coronavirus cases using surveillance camera footage and geo-location data.

Market Growth Factors

Increased Demand for Cloud Computing and a Boom in Big Data

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 that supply enterprise storage solutions. Data analysis is performed online using cloud storage, giving the advantage of evaluating real-time data collected on the cloud.

Cloud computing enables data analysis from any location and at any time. Moreover, using the cloud to deploy machine learning allows businesses to get useful data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, lowering infrastructure and storage costs. As a result, the machine learning as a service business is growing as cloud computing technology becomes more widely adopted.

Use of 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 the 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 & 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 in order to produce new creative technology.

Market Restraining Factors

Technical Restraints and Inaccuracies of ML

The ML platform provides a plethora of advantages that aid in market expansion. However, several parameters on the platform are projected to impede market expansion. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the market's primary constraining factors.

In the big data and machine learning manufacturing industries, precision is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This is expected to exorbitantly increase the operational costs for the owner of the manufacturing unit than decrease it.

Key Topics Covered:

Chapter 1. Market Scope & Methodology

Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints

Chapter 3. Competition 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 Expansions
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 Move: (Product Launches and Product Expansions : 2018, Jan - 2022, May) Leading Players
3.4.3 Key Strategic Move: (Partnership, Collaboration and Agreement : 2019, Apr - 2022, Mar) Leading Players

Chapter 4. Global Machine learning as a Service Market by End User
4.1 Global IT & 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 & Utilities Market by Region
4.7 Global Public Sector Market by Region
4.8 Global Aerospace & Defense Market by Region
4.9 Global Other End User Market by Region

Chapter 5. Global Machine learning as a Service Market by Offering
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 Enterprises Market by Region
6.2 Global Small & Medium Enterprises Market by Region

Chapter 7. Global Machine learning as a Service Market by Application
7.1 Global Marketing & Advertising Market by Region
7.2 Global Fraud Detection & Risk Management Market by Region
7.3 Global Computer vision Market by Region
7.4 Global Security & 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 & 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 Company Overview
9.1.2 Financial Analysis
9.1.3 Segmental and Regional Analysis
9.1.4 Research & Development Expense
9.1.5 Recent strategies and developments:
9.1.5.1 Product Launches and Product Expansions:
9.1.5.2 Acquisition and Mergers:
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 & Development Expense
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 & Development Expense
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 Segmental 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 & Segmental Analysis
9.5.4 Research & 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 & 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 & 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

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• Global Machine learning as a Service Market