The global machine learning as a service (MLaaS) market reached a value of US$ 3.90 Billion in 2021. Looking forward, IMARC Group expects the market to reach US$ 25.67 Billion by 2027, exhibiting a CAGR of 36.80% during 2022-2027. Keeping in mind the uncertainties of COVID-19, we are continuously tracking and evaluating the direct as well as the indirect influence of the pandemic on different end use industries. These insights are included in the report as a major market contributor.
Machine learning as service (MLaaS) refers to a set of cloud-based platforms that offer machine learning (ML) tools on the go. It offers the benefits of cost-effective data storage, pre-made pipelines for data ingestion, and toolkits for setting up effective data governance. Moreover, it plays a vital role in eradicating infrastructural concerns, such as model training and evaluation and pre-processing. It also enables deep learning, data visualization, accurate regression, speech and face recognition, predictive analytics, and computer vision. Consequently, organizations nowadays use MLaaS to meet the dynamic business needs, such as personalizing search results and predicting product demand.
Machine Learning as a Service (MLaaS) Market Trends:
Several companies presently are transferring their data from on-premise storage to cloud storage systems. This acts as a key factor driving the need for MLaaS to organize the data properly. Apart from this, the widespread adoption of Internet of Things (IoT) devices and systems in enterprise automation is propelling the market growth. Besides this, the escalating demand for personalization and developments in virtual technologies, such as virtual assistants, augmented reality (AR), smart storage management, and logistics, is contributing to the market growth. Furthermore, MLaaS is gaining traction in designing chatbots and virtual assistants, quality control and detection of manufacturing defects, automating business documentation workflow, and natural language processing (NLP) tasks. Additionally, as ML can detect disease and virus infections accurately, it is used to automate the task of predicting coronavirus disease (COVID-19) infection and forecast future infection tallies. In line with this, scientists across the globe are focusing on developing ML-based smart solutions. These solutions assist in diagnosing and treating various diseases at an early stage, which is anticipated to drive the market.
Key Market Segmentation:
IMARC Group provides an analysis of the key trends in each sub-segment of the global machine learning as a service (MLaaS) market, along with forecasts at the global, regional and country level from 2022-2027. Our report has categorized the market based on component, organization size, application and end user.
Breakup by Component:
Breakup by Organization Size:
Small and Medium-sized Enterprises
Breakup by Application:
Marketing and Advertising
Fraud Detection and Risk Management
Augmented and Virtual Reality
Natural Language Processing
Security and Surveillance
Breakup by End User:
IT and Telecom
Aerospace and Defense
Breakup by Region:
Middle East and Africa
The competitive landscape of the industry has also been examined along with the profiles of the key players being Amazon.com Inc., Bigml Inc., Fair Isaac Corporation, Google LLC (Alphabet Inc.), H2O.ai Inc., Hewlett Packard Enterprise Development LP, Iflowsoft LLC, International Business Machines Corporation, Microsoft Corporation, MonkeyLearn, Sas Institute Inc. and Yottamine Analytics Inc. Key Questions Answered in This Report:
How has the global machine learning as a service (MLaaS) market performed so far and how will it perform in the coming years?
What has been the impact of COVID-19 on the global machine learning as a service (MLaaS) market?
What are the key regional markets?
What is the breakup of the market based on the component?
What is the breakup of the market based on the organization size?
What is the breakup of the market based on the application?
What is the breakup of the market based on the end user?
What are the various stages in the value chain of the industry?
What are the key driving factors and challenges in the industry?
What is the structure of the global machine learning as a service (MLaaS) market and who are the key players?
What is the degree of competition in the industry?