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

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  • November 24, 2022
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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 AnalysisThe 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 FactorsIncreased Demand for Cloud Computing and a Boom in Big DataThe 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 SystemsMachine 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 FactorsTechnical Restraints and Inaccuracies of MLThe 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. Report AttributeDetailsNo. of Pages337Forecast Period2021 – 2028Estimated Market Value (USD) in 2021$5515 MillionForecasted Market Value (USD) by 2028$36204 MillionCompound Annual Growth Rate31.6%Regions CoveredGlobal Key Topics Covered: Chapter 1. Market Scope & MethodologyChapter 2. Market Overview2.1 Introduction2.1.1 Overview2.1.1.1 Market Composition and Scenario2.2 Key Factors Impacting the Market2.2.1 Market Drivers2.2.2 Market RestraintsChapter 3. Competition Analysis – Global3.1 KBV Cardinal Matrix3.2 Recent Industry Wide Strategic Developments3.2.1 Partnerships, Collaborations and Agreements3.2.2 Product Launches and Product Expansions3.2.3 Acquisition and Mergers3.3 Market Share Analysis, 20213.4 Top Winning Strategies3.4.1 Key Leading Strategies: Percentage Distribution (2018-2022)3.4.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Jan – 2022, May) Leading Players3.4.3 Key Strategic Move: (Partnership, Collaboration and Agreement : 2019, Apr – 2022, Mar) Leading PlayersChapter 4. Global Machine learning as a Service Market by End User4.1 Global IT & Telecom Market by Region4.2 Global BFSI Market by Region4.3 Global Manufacturing Market by Region4.4 Global Retail Market by Region4.5 Global Healthcare Market by Region4.6 Global Energy & Utilities Market by Region4.7 Global Public Sector Market by Region4.8 Global Aerospace & Defense Market by Region4.9 Global Other End User Market by RegionChapter 5. Global Machine learning as a Service Market by Offering5.1 Global Services Only Market by Region5.2 Global Solution (Software Tools) Market by RegionChapter 6. Global Machine learning as a Service Market by Organization Size6.1 Global Large Enterprises Market by Region6.2 Global Small & Medium Enterprises Market by RegionChapter 7. Global Machine learning as a Service Market by Application7.1 Global Marketing & Advertising Market by Region7.2 Global Fraud Detection & Risk Management Market by Region7.3 Global Computer vision Market by Region7.4 Global Security & Surveillance Market by Region7.5 Global Predictive analytics Market by Region7.6 Global Natural Language Processing Market by Region7.7 Global Augmented & Virtual Reality Market by Region7.8 Global Others Market by RegionChapter 8. Global Machine learning as a Service Market by RegionChapter 9. Company Profiles9.1 Hewlett Packard Enterprise Company9.1.1 Company Overview9.1.2 Financial Analysis9.1.3 Segmental and Regional Analysis9.1.4 Research & Development Expense9.1.5 Recent strategies and developments:9.1.5.1 Product Launches and Product Expansions:9.1.5.2 Acquisition and Mergers:9.2 Oracle Corporation9.2.1 Company Overview9.2.2 Financial Analysis9.2.3 Segmental and Regional Analysis9.2.4 Research & Development Expense9.2.5 SWOT Analysis9.3 Google LLC9.3.1 Company Overview9.3.2 Financial Analysis9.3.3 Segmental and Regional Analysis9.3.4 Research & Development Expense9.3.5 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 Overview9.4.2 Financial Analysis9.4.3 Segmental Analysis9.4.4 Recent strategies and developments:9.4.4.1 Partnerships, Collaborations, and Agreements:9.4.4.2 Product Launches and Product Expansions:9.5 IBM Corporation9.5.1 Company Overview9.5.2 Financial Analysis9.5.3 Regional & Segmental Analysis9.5.4 Research & Development Expenses9.5.5 Recent strategies and developments:9.5.5.1 Partnerships, Collaborations, and Agreements:9.6 Microsoft Corporation9.6.1 Company Overview9.6.2 Financial Analysis9.6.3 Segmental and Regional Analysis9.6.4 Research & Development Expenses9.6.5 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 Overview9.7.2 Financial Analysis9.7.3 Segmental and Regional Analysis9.7.4 Research & Development Expenses9.8 SAS Institute, Inc.9.8.1 Company Overview9.8.2 Recent strategies and developments:9.8.2.1 Partnerships, Collaborations, and Agreements:9.9 Yottamine Analytics, LLC9.9.1 Company Overview9.10. BigML9.10.1 Company Overview For more information about this report visit https://www.researchandmarkets.com/r/f69w74
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Global Machine learning as a Service Market

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