3 Key Insights on Competitive Landscape in Global Machine Learning as a Service (MLaaS) Market: Ken Research

Few Diverse Global Players Dominate the Market Holding Majority Revenue Share Despite the Presence of about ~150 Competitors Comprising a Large Number of Country-Niche Players and Some Specialist Players, finds a recent market study on the Global Machine Learning as a Service (MLaaS) Market by Ken Research

Machine Learning as a Service (MLaaS) is a group of services that provide machine learning (ML) tools as a component of cloud computing solutions. MLaaS enables customers/clients to benefit from ML without the associated expense, risk, or time required to build an internal ML team.

MLaaS is used in processes such as risk analytics, fraud detection, manufacturing, supply chain optimization, and others. It offers the freedom to build in-house infrastructure from scratch and manage and store data. It has a synergistic value in engaging data with the cloud and can revolutionize a paradigm of ML for a specific result.

"Ken Research shares 3 key insights on competitive landscape in this high opportunity market from its latest research study."

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Country Niche Players Constitute ~45% of the Total Number of Competitors, While Regional Companies Represent a 35% Share of Competitors in MLaaS Market

A comprehensive competitive analysis conducted during the Research Study found that the Global Machine Learning as a Service (MLaaS) market is highly competitive with ~150 players, including globally diversified players, regional players, and a large number of country-niche players with their niche in cloud-based solutions for multiple end-user industries. The majority of the top 12 global cloud-based solution companies, including Amazon, Google, IBM, Microsoft, Oracle, HPE, SAS Institution, FICO, Yottamine Analytics, LLC, PREDICTION LABS LTD, BigML, and ersatz Labs, Inc. are expected to maintain their leading positions during the forecast period. The majority of the country niche players offer end-to-end solutions to some IoT platform suppliers who use ML technologies to strengthen their operating management skills in order to secure rein in vast IoT systems. IBM, Microsoft, Oracle, HPE, and SAS Institution are among the key players in the Global Machine Learning as a Service (MLaaS) Market.

Regional Players' Ongoing Efforts to Provide IoT Solutions to End-User Industries Drive Their Revenue Growth

Detailed comparative analysis of key competitors available within the Research Study shows that numerous ML service providers such as Amazon, IBM, HPE, Google, and others are highly focused on providing a substantial cloud-based solutions and network security that can be used across several end-user industries. Furthermore, Numerous companies and organizations are aggressively investing in advanced technology, software, and network security activities. Major companies collaborate and launch products for the development of new technology in ML.

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In November 2021, SAS added support for open-source users to its flagship SAS Viya platform. SAS Viya is for open-source integration and utility. The software user established an API-first strategy that fueled a data preparation process with ML.
In February 2022 - Telecom giant AT&T and AI Company H2O collaborated and launched an AI feature for enterprises. This delivers a repository for collaborating, sharing, reusing, and discovering ML features to speed AI project deployments and improve ROI.
Global Machine Learning as a Service Industry Outlook

Increasing Government Initiatives, Strategies, and Investments in Data Visualization, and Advanced Machine Learning Propels Market Growth.

In July 2020, Hewlett Packard released HPE Ezmeral, a new brand and software portfolio developed to assist enterprises to quicken digital transformation across their organization, from edge to cloud. The HPE Ezmeral goes from a portfolio consisting of container orchestration and management, AI/ML, and data analytics to cost control, IT automation and AI-driven operations, and security.
In December 2021, AWS announced six new Amazon SageMaker capabilities. This makes ML more accessible and cost-effective. This brings new capabilities, including a no-code environment for creating accurate ML predictions and more accurate data labeling using highly skilled annotators.
In March 2022, Google entered into a partnership with BT, a British telecommunications company. Under the partnership, BT utilized a suite of Google Cloud products and services-including cloud infrastructure, ML and AI, data analytics, security, and API management-to offer excellent customer experiences, decrease costs, and risks, and create more revenue streams. Google aimed to enable BT to get access to hundreds of new business use cases to solidify its goals around digital offerings and developing hyper-personalized customer engagement.
In April 2021, Microsoft Corporation released an open dataset for health & genomics, transportation, labor & economics, supplementary, population & safety, and common datasets. This dataset aims to increase the accuracy of machine learning models using publicly accessible datasets. This also enables businesses to use Azure Open Datasets with its machine learning and data analytics solutions to offer insights at hyper-scale, which increases sales of these businesses' ML as a Service.

Related Report
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https://www.kenresearch.com/business-research/europe-saas-based-scm-market-outlook-to-2027/

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