Kenneth Research, in its repository of market research reports, have recently added a report on Global Machine Learning as a Service Market which emphasizes on the latest trends, key opportunities, drivers, and the challenges associated with the growth of the market during the forecast period, i.e., 2022-2031. The Global Machine Learning as a Service Market is anticipated to grow primarily on account of the growing trade of ICT goods and services worldwide. According to the statistics by the World Bank, the exports of ICT goods globally increased from 11.164% of total goods exports in 2017 to 11.53% of total goods exports in 2019.
U.S. Market recovers fast; In a release on May 4th 2021, the U.S. Bureau and Economic Analsysis and U.S. Census Bureau mentions the recovery in the U.S. International trade in March 2021. Exports in the country reached $200 billion, up by $12.4 billion in Feb 2021. Following the continuous incremental trend, imports tallied at $274.5 billion, picked up by $16.4 billion in Feb 2021. However, as COVID19 still haunts the economies across the globe, year-over-year (y-o-y) avergae exports in the U.S. declined by $7.0 billion from March 2020 till March 2021 whilest imports increased by $20.7 billion during the same time. This definitely shows how the market is trying to recover back and this will have a direct impact on the Healthcare/ICT/Chemical industries, creating a huge demand for Global Machine Learning as a Service Market products.
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Moreover, increase in ICT spending around the globe for hardware, software, services, new technologies and telecom among others is also anticipated to contribute to the growth of the market in the coming years. The spending on new technologies recorded around USD 0.6 million in 2018 and is further estimated to reach close to USD 1.3 million by 2023. The total ICT spending around the globe further recorded close to USD 5 million in 2019 and is further projected to touch USD 6 million by 2023.
The Global Machine Learning as a Service Market is expected to grow with a significant CAGR during the forecast period, i.e. 2021-2023, on the back of growing internet penetration around the world along with the rising adoption of smartphone. The statistics by the GSMA Intelligence stated that the total unique mobile subscribers as on May 2020 around the world was 5.24 billion.
“Final Report will add the analysis of the impact of COVID-19 on this industry.”
Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfill reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.
According to Kenneth Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 2017–2023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.
Segmentation by Components:
The market has been analyzed and segmented by the following components – Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.
Segmentation by End-users
The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.
Segmentation by Deployment Mode
The market has been analyzed and segmented by the following deployment mode, namely public and private.
The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. China’s dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.
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Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.
The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
• The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
• It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategy, and views.
• The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
• In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.
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