Global Machine Learning as a Service Market is valued at USD 15.47 Billion in 2021 and expected to reach USD 157.48 Billion by 2028 with a CAGR of 39.3% over the forecast period.
Global Machine Learning as a Service Market: Global Size, Trends, Competitive, Historical & Forecast Analysis, 2022-2028- Increasing demand for artificial intelligence, growing adoption of cloud-based technologies, growing advancements in this field are some of the major factors driving the growth of Global Machine Learning as a Service Market.
Machine learning (ML) is a branch of artificial intelligence (AI) that allows training computers to generate classifications or predictions using statistical approaches, enabling data mining initiatives to find key insights. These insights drive application and corporate decision-making, ideally affecting key growth KPIs. Because it is based on algorithms, model complexity, and computational complexity, it necessitates the development of trained people. Machine learning performance has risen rapidly as data science and artificial intelligence have advanced. Companies are recognising the potential of this technology, and hence its adoption rate is likely to rise throughout the projection period. Machine learning solutions are now available on a subscription basis, making it easier for customers to take use of this technology. Furthermore, it enables pay-as-you-go flexibility. Deep learning approaches for predictive analytics are used in machine learning-as-a-service to improve decision-making. Using MLaaS, on the other hand, creates security concerns for ML model owners as well as data privacy challenges for data owners. Data owners are concerned about their data's privacy and security on MLaaS platforms. Machine learning allows computers to learn and adjust their analytical capabilities when exposed to new data sets without having to be explicitly programmed. Several causes are driving the expansion of machine learning and its accompanying advanced computing and analytics markets. Some of these include increased need for mapping client behaviour, particularly in the marketing and advertising sectors, increased security concerns, and a growing requirement of applications for support during emergency.
Considering cloud service flexibility can meet unanticipated surges in service demand, the COVID-19 epidemic prompted many enterprises to expedite their migrations to public cloud solutions. Migrations to the cloud aided enterprises in reinventing their business practises in the aftermath of COVID-19. The need for AI services has increased, and several cloud providers now supply AIaaS and MLaaS.
The global machine learning as a service market is segmented based on the organization size, application, and end user. Based on organization size, the global machine learning as a service market is segmented into small and medium enterprises and large enterprises. Based on application, the market is segmented into automated network management, predictive maintenance, marketing and advertisement, fraud detection and risk analytics, and others. By end-user, the global Machine Learning as a Service market is segmented into IT & telecom, automotive, healthcare, aerospace & defense, retail, government, BFSI, and others.
Global MLaaS is segmented on the basis of component, organization size, end-use industry, application, and region & country level. Based upon the component, Machine Learning as a Service market is classified into software, and services. Based upon organization size, the Machine Learning as a Service market is divided into large enterprises, and small & medium enterprises. Based on end-use industry, the global Machine Learning as a Service market is classified into aerospace & defense, IT & telecom, energy & utilities, public sector, manufacturing, BFSI, healthcare, retail, and others. Based on application, the global Machine Learning as a Service market is classified into marketing & advertising, fraud detection & risk management, predictive analytics, augmented & virtual reality, natural language processing, computer vision, security & surveillance, and others.
The regions covered in the global Machine Learning as a Servicemarket report are North America, Europe, Asia-Pacific, Latin America, and the rest of the World. Based on country level, the market of global Machine Learning as a Serviceis sub divided into U.S., Mexico, Canada, U.K., France, Germany, China, Italy, India, Japan, Southeast Asia, Middle East Asia (Saudi Arabia, UAE, Egypt) GCC, Africa, etc.
Zendesk Launches New Customer Sentiment and Intent Functionality Powered by Machine Learning
On September 14th 2022; Intelligent Triage and Smart Assist, powered by Machine Learning, new AI solutions from Zendesk, Inc were launched. These solutions are equipped with the ability to enable businesses to automatically triage customer support requests and access valuable data at scale. The technology employs a vertical lens and proprietary industry expertise and insights from trillions of customer data points.
Silent Eight Expanding Partnership with HSBC to deploy Machine Learning
On October 11th 2022; Silent Eight expanded its existing partnership with HSBC to combat financial crime, which will include the deployment of Negative News Screening, which uses machine learning to identify individuals who are more likely to engage in money laundering, fraud, or terrorist financing. Silent Eights' solution addresses true matches more effectively and resolves the issue of false identifications.
Agilent Technologies Inc. Acquired AI and Machine Learning Services to Enhance Lab Productivity
On February 17th 2022; Agilent Technologies Inc. acquired superior artificial intelligence (AI) technology created by Virtual Control, a producer of AI and machine learning software that delivers unique lab testing analysis solutions. Agilent integrated the ACIES software into its industry-leading gas chromatography and mass spectrometry (GS/MS) systems to boost high-throughput lab productivity, efficiency, and accuracy.
The machine learning as a service market is expanding owing to the increased acceptance of cloud computing technologies as well as the use of social media platforms. Cloud computing has been widely adopted by all firms that supply corporate storage solutions. The data analysis is conducted online while using cloud storage, giving the advantage of evaluating real-time data created on the cloud. Cloud computing enables data analysis from any location and at any time. Furthermore, deploying machine learning through the cloud enables businesses to obtain valuable information, such as customer behaviour and purchasing trends, virtually from linked data warehouses, lowering the cost of infrastructure and storage. As a result, the increased usage of cloud computing technologies fuels the expansion of the machine learning as a service business. As a result, cloud computing companies provide machine learning as a service (or MLaaS) technologies to automate time-consuming manual procedures, handle data more effectively, decrease human mistakes, and promote continuous development. Machine learning may be utilised for anything from corporate forecasting to spam detection to enhanced customer service, which helps to drive the market's worldwide demand.
Furthermore, the healthcare industry has a long history of being an early adopter of technology improvements. Machine learning is a subset of artificial intelligence that is now used in numerous health breakthroughs, such as the creation of novel medical procedures, the processing of patient data and records, and the treatment of chronic diseases. Some machine learning businesses are researching how to organise and convey patient information to clinicians during telemedicine meetings, as well as record information during virtual visits to simplify processes. Machine learning is also used by pharmaceutical businesses to aid in drug research and development, fuelling the global rise of machine learning as a service.
The Artificial Intelligence (AI) system incorporates machine learning-powered learning, reasoning, and self-correction. The instances of AI applications are expert systems, speech recognition, and machine vision. Current efforts, such as big data infrastructure and cloud computing, are to blame for AI's growing popularity. Top companies in various sectors, such as Google, Microsoft, Amazon (Software & IT); Bloomberg, American Express (Financial Services); and Tesla, 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 offered financial support to young start-ups in order to develop new creative technology. Google, for instance, invested USD 10.5 million in Algorithm, a marketplace and corporate solution that enables developers to quickly access its library of 3,500 algorithms, functions, and machine learning models.
However, the absence of sufficient infrastructure and regulatory difficulties, as well as data breach and privacy risks with machine learning as a service, may stymie industry expansion. Despite this, rising need for good connection and technological improvement may open up new avenues for market expansion.
In terms of technical breakthroughs and usage, North America is the fastest expanding area in the worldwide machine learning as a service industry. It has a well-developed infrastructure and can afford machine learning as a service solution. Furthermore, increased military sector spending, as well as technical advances in the telecommunications industry, are likely to boost market expansion throughout the forecast period. Government data security requirements are projected to be a significant driver of the machine learning services industry. The market is expected to be driven by services such as security information and cloud applications. Furthermore, the rise of artificial intelligence and cognitive computing is predicted to open up attractive prospects for market participants to exploit various industrial applications such as predictive analytics, natural language processing, computer vision, and fraud detection and management.
However, Asia-Pacific is predicted to be the fastest expanding geographical sector with the greatest CAGR throughout the projection period. Leading firms are concentrating on Asia-Pacific to expand their company since the area is likely to see rapid development in security service deployment, particularly in the banking, financial services, and insurance (BFSI) sector. During the projected period, Asia-Pacific is predicted to be the fastest expanding regional sector, with the highest CAGR. Participants in the industry are recognizing the significance of providing multi-modal platforms to provide improved customer service. The rise in AI application penetration is projected to be the primary trend impacting market growth in this area. Nevertheless, important measures have been implemented by government entities to accelerate the use of machine learning and related technologies in this region.
Middle East and Africa
|2015 - 2021
|2022 - 2028
|Market Size in 2021:
|USD 15.47 Billion
|Base year considered
|Forecast Period CAGR %:
|Market Size Expected in 2028:
|USD 157.48 Billion
|Tables, Charts & Figures:
|Microsoft Corporation, IBM Corporation, Google LLC, SAS Institute Inc., Fair Isaac Corporation (FICO), Hewlett Packard Enterprise Company, Yottamine Analytics LLC, Amazon Web Services Inc., BigML Inc., and Iflowsoft Solutions Inc.
|By Organization Size, By Application, By End-User.
|North America, U.S., Mexico, Canada, Europe, UK, France, Germany, Italy, Asia Pacific, China, Japan, India, Southeast Asia, South America, Brazil, Argentina, Columbia, The Middle East and Africa, GCC, Africa, Rest of the Middle East and Africa
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