The global printed electronic materials industry is expected to reach $17.5 billion by 2031 at a CAGR of 18.82%

The global machine learning-as-a-service industry is expected to reach $36.2 billion by 2028

DUBLIN, December 6, 2022 /PRNewswire/ — The report “Global Machine Learning as a Service Market Size, Share & Industry Trends Analysis Report By End User, By Offer, By Organization Size, By Application, By Regional Outlook and Forecast, 2022-2028” has been added at from offer.

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The Global Machine Learning as a Service Market Size is Expected to Reach $36.2 billion by 2028, growing the market at 31.6% CAGR during the forecast period.

Machine learning is a data analysis method that includes statistical data analysis to create the desired prediction output without using explicit programming. It uses a sequence of algorithms to understand the relationship between data sets to produce the desired result. It is designed to include artificial intelligence (AI) and cognitive computing features. Machine learning as a service (MLaaS) refers to a group of cloud computing services that provide machine learning technologies.

Increased demand for cloud computing, along with growth related to artificial intelligence and cognitive computing, are the major growth drivers for the service industry. Growth in demand for cloud-based solutions such as cloud computing, rise in adoption of analytical solutions, growth in artificial intelligence and cognitive computing market, rise in domains applications and the scarcity of trained professionals all influence 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 increases. Since MLaaS platforms are essentially cloud providers, they enable solutions to appropriately manage data for machine learning experiments and data pipelines, making it easier to access and process data. for data engineers.

For organizations, MLaaS providers offer features like data visualization and predictive analytics. They also provide APIs for sentiment analysis, facial recognition, credit scores, business intelligence, and healthcare, among others. The actual calculations for these processes are mined by MLaaS providers, so data scientists don’t have to worry about them. For machine learning experimentation and model building, some MLaaS vendors even offer 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 claimed millions of lives around the world and left economic and financial systems in tatters. Individuals can benefit from knowledge about individual-level susceptibility variables to better understand and cope with their psychological, emotional, and social well-being.

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

Market Growth Factors

Rise in demand for cloud computing and big data boom

The industry is growing due to the growing acceptance of cloud computing technologies and the use of social media platforms. Cloud computing is now widely used by all companies that provide enterprise storage solutions. Data analysis is performed online using cloud storage, which offers the advantage of real-time evaluation of data collected on the cloud.

Cloud computing enables data analysis from anywhere and at any time. Additionally, using the cloud to deploy machine learning enables businesses to obtain valuable data, such as consumer behavior and purchasing trends, virtually from linked data warehouses, reducing infrastructure and storage costs. As a result, machine learning as a service is growing as cloud computing technology is increasingly 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 machine vision are examples of AI applications. The growing popularity of AI is due to current efforts such as big data infrastructure and cloud computing.

Top companies from all industries, including Google, Microsoft and Amazon (software and IT); Bloomberg, American Express (Financial Services); and Tesla and Ford (automotive), identified AI and cognitive computing as a key strategic driver and began 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 restraining factors

Technical constraints and inaccuracies of ML

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

In the big data and machine learning manufacturing industries, accuracy is crucial. A minor flaw in the algorithm could result in incorrect items being produced. This should exorbitantly increase operational costs for the manufacturing unit owner rather than decrease them.

Main topics covered:

Chapter 1. Market Scope and Methodology

Chapter 2. Market Overview
2.1 Presentation
2.1.1 Presentation Market composition and scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market constraints

Chapter 3. Competitive Analysis – Global
3.1 Cardinal Matrix KBV
3.2 Recent Industry-Wide Strategic Developments
3.2.1 Partnerships, collaborations and agreements
3.2.2 Product launches and product extensions
3.2.3 Acquisitions and mergers
3.3 Market Share Analysis, 2021
3.4 Main winning strategies
3.4.1 Key Primary Strategies: Percentage Breakdown (2018-2022)
3.4.2 Key Strategic Movement: (Product Launches and Product Extensions: 2018, Jan – 2022, May) Key Players
3.4.3 Key Strategic Movement: (Partnership, Collaboration and Agreement: 2019, April – 2022, March) Key Players

Chapter 4. Global Machine Learning as a Service Market by End User
4.1 Global Information and Telecommunications Technology 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 and 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 Offer
5.1 Global Services Only Market by Region
5.2 Global Solutions (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 & Medium Enterprise Market by Region

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. Business Profiles
9.1 Hewlett Packard Enterprise Company
9.1.1 Company Overview
9.1.2 Financial analysis
9.1.3 Sectoral and regional analysis
9.1.4 Research and development costs
9.1.5 Strategies and recent developments: Product launches and product extensions: Acquisition and merger:
9.2 Oracle Corporation
9.2.1 Company overview
9.2.2 Financial analysis
9.2.3 Sectoral 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 Sectoral and regional analysis
9.3.4 Research and development costs
9.3.5 Strategies and recent developments: Partnerships, collaborations and agreements: Product launches and product extensions:
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 Strategies and recent developments: Partnerships, collaborations and agreements: Product launches and product extensions:
9.5 IBM Corporation
9.5.1 Company Overview
9.5.2 Financial analysis
9.5.3 Regional and sector analysis
9.5.4 Research and development costs
9.5.5 Recent Strategies and Developments: Partnerships, collaborations and agreements:
9.6 Microsoft Corporation
9.6.1 Company Overview
9.6.2 Financial analysis
9.6.3 Sectoral and regional analysis
9.6.4 Research and development costs
9.6.5 Recent Strategies and Developments: Partnerships, collaborations and agreements: Product launches and product extensions:
9.7 Fair Isaac Corporation (FICO)
9.7.1 Company Overview
9.7.2 Financial analysis
9.7.3 Sectoral and regional analysis
9.7.4 Research and development costs
9.8 SAS Institute, Inc.
9.8.1 Company Overview
9.8.2 Strategies and recent 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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