Machine Learning as a Service Market – Global Drivers, Restraints, Opportunities, Trends, and Forecasts up to 2023
Machine Learning as a Service in Manufacturing Market – Global Drivers, Restraints, Opportunities, Trends, and Forecasts up to 2023
Market Overview
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, fulfil 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.
Market Analysis
According to Infoholic 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.
Regional Analysis
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.
Vendor Analysis
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.
Benefits
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.
Market Overview
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, fulfil 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.
Market Analysis
According to Infoholic 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.
Regional Analysis
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.
Vendor Analysis
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.
Benefits
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.
LIST OF TABLES
Table 1 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY REGIONS, 2016-2022 ($MILLION)
Table 2 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY COMPONENTS, 2016-2022 ($MILLION)
Table 3 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY DEPLOYMENT MODE, 2016-2022 ($MILLION)
Table 4 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY END-USERS, 2016-2022 ($MILLION)
Table 1 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY REGIONS, 2016-2022 ($MILLION)
Table 2 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY COMPONENTS, 2016-2022 ($MILLION)
Table 3 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY DEPLOYMENT MODE, 2016-2022 ($MILLION)
Table 4 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY END-USERS, 2016-2022 ($MILLION)
LIST OF CHARTS
Chart 2 RESEARCH METHODOLOGY
Chart 3 PORTERS 5 FORCES ON MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET
Chart 4 STEPS IN MACHINE LEARNING PROCESS
Chart 5 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET SEGMENTATION
Chart 6 MARKET DYNAMICS – DRIVERS, RESTRAINTS & OPPORTUNITIES
Chart 7 DRO - IMPACT ANALYSIS OF MACHINE LEARNING AS A SERVICE IN MANUFACTURING
Chart 8 TECHNOLOGY ROADMAP FOR MACHINE LEARNING AS A SERVICE IN MANUFACTURING
Chart 9 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY GEOGRAPHIES
Chart 10 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN AMERICAS
Chart 11 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN EUROPE
Chart 12 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN APAC
Chart 13 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN MEA
Chart 14 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY COMPONENTS ($ MILLION)
Chart 15 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY DEPLOYMENT MODE ($ MILLION)
Chart 16 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY END-USERS ($ MILLION)
Chart 18 MICROSOFT CORPORATION: SWOT ANALYSIS
Chart 2 RESEARCH METHODOLOGY
Chart 3 PORTERS 5 FORCES ON MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET
Chart 4 STEPS IN MACHINE LEARNING PROCESS
Chart 5 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET SEGMENTATION
Chart 6 MARKET DYNAMICS – DRIVERS, RESTRAINTS & OPPORTUNITIES
Chart 7 DRO - IMPACT ANALYSIS OF MACHINE LEARNING AS A SERVICE IN MANUFACTURING
Chart 8 TECHNOLOGY ROADMAP FOR MACHINE LEARNING AS A SERVICE IN MANUFACTURING
Chart 9 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY GEOGRAPHIES
Chart 10 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN AMERICAS
Chart 11 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN EUROPE
Chart 12 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN APAC
Chart 13 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN MEA
Chart 14 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY COMPONENTS ($ MILLION)
Chart 15 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY DEPLOYMENT MODE ($ MILLION)
Chart 16 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY END-USERS ($ MILLION)
Chart 18 MICROSOFT CORPORATION: SWOT ANALYSIS
