Insights on the Machine Learning as a Service Global 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, By Application, By Regional Outlook and Forecast, 2022 – 2028” report added to by offering.

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

Machine learning is a data analysis method that involves analyzing statistical data to generate the desired predictive output without using explicit programming. It uses a series of algorithms to understand the relationship between datasets to produce the desired results. It is designed to include artificial intelligence (AI) and cognitive computing functions. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

The increased demand for cloud computing, as well as the growth connected to artificial intelligence and cognitive computing, are the major machine learning as drivers of growth in the service industry. The growth in demand for cloud-based solutions, such as cloud computing, increasing adoption of analytical solutions, market development in artificial intelligence and cognitive computing, additional application areas, and lack of trained professionals all influence machine learning as a service. market.

As more businesses migrate their data from on-premise storage to cloud storage, the need for efficient data organization will grow. Because MLaaS platforms are essentially cloud providers, they enable proper data management solutions 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 rating, corporate intelligence, and healthcare, among other things. The actual calculations of these processes are taken over by the MLaaS providers, so data scientists don’t have to worry about it. For machine learning experimentation and model building, some MLaaS providers even have a drag-and-drop interface.

Analysis of the Impact of COVID-19

The COVID-19 pandemic has had a major impact on many countries’ health, economic, and social systems. It resulted in millions of deaths worldwide and left economic and financial systems in ruins. Individuals can benefit from knowledge about individual-level behavioral 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. Cases of COVID-19 are tracked and tracked in many countries using population monitoring methods powered by machine learning and artificial intelligence. Researchers in South Korea, for example, are tracking coronavirus cases using surveillance camera footage and geo-location data.

Factors in Market Development

Increased Demand for Cloud Computing and a Big Data Boom

The industry is growing due to increased acceptance of cloud computing technologies and use of social media platforms. Cloud computing is now widely used by all companies that supply business storage solutions. Data analysis is performed online using cloud storage, which provides the advantage of evaluating real-time data collected in the cloud.

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

Using Machine Learning to Troubleshoot Artificial Intelligence Systems

Machine learning is used to drive 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 efforts such as big data infrastructure and cloud computing.

Leading companies across industries, including Google, Microsoft, and Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (Automotive), recognized AI and cognitive computing as a key strategic driver and began investing in machine learning to develop more advanced systems. These leading companies also provide financial support to young startups to develop new creative technology.

Causes of Market Restraint

Technical Limitations and Pitfalls of ML

The ML platform provides many advantages that help expand the market. However, several parameters of the platform are expected to hinder the expansion of the market. The presence of inaccuracy in these algorithms, which are sometimes immature and underdeveloped, is one of the main reasons for restraining the market.

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In the big data and machine learning manufacturing industry, accuracy is critical. A small error in the algorithm can result in incorrect objects being produced. It is expected that this will increase the operating cost for the owner of the manufacturing unit rather than reducing it.

Report Attribute Details
Number of Pages 337
Prediction Time 2021 – 2028
Estimated Market Value (USD) in 2021 $5515 Million
Predicted Market Value (USD) in 2028 $36204 Million
Compound Annual Growth Rate 31.6%
Regions Covered Whole world

Main Topics:

Chapter 1. Market Scope and Procedure

Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview Market Composition and Scenario
2.2 Main Factors Affecting the Market
2.2.1 Market Drivers
2.2.2 Market Restrictions

Chapter 3. Competition Analysis – Worldwide
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 Expansion
3.2.3 Acquisition and Merger
3.3 Market Share Analysis, 2021
3.4 Key Winning Strategies
3.4.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.4.2 Key Strategic Activities: (Product Launch and Product Expansion : 2018, Jan – 2022, May) Top Players
3.4.3 Key Strategic Activities: (Partnership, Collaboration and Agreement : 2019, Apr – 2022, Mar) Leading Players

Chapter 4. General Machine Learning as an End-User Service Market
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. General Machine Learning as a Service Market by Offering
5.1 Global Service Only Market by Region
5.2 Global Solution (Software Tools) Market by Region

Chapter 6. General Machine Learning as a Service Market by Organization Size
6.1 Global Large Enterprise Market by Region
6.2 Global Small and Medium Business Market by Region

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Chapter 7. General 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 Other Market by Region

Chapter 8. General 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 Costs
9.1.5 New strategies and developments: Product Launch and Product Expansion: Acquisition and Integration:
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 Costs
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 Costs
9.3.5 New strategies and developments: Partnerships, Collaborations, and Agreements: Product Launches and Product Expansion:
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 New strategies and developments: Partnerships, Collaborations, and Agreements: Product Launches and Product Expansion:
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 New strategies and developments: 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 New strategies and developments: Partnerships, Collaborations, and Agreements: 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 New strategies 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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