Global Smart Mobility Market : 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.

 1 Industry Outlook 9
 1.1 Industry Overview 9
 1.2 Industry Trends 10
 1.3 Pest Analysis 13
 2 Report Outline 15
 2.1 Report Scope 15
 2.2 Report Summary 16
 2.3 Research Methodology 17
 2.4 Report Assumptions 18
 3 Market Snapshot 19
 3.1 Total Addressable Market (TAM) 19
 3.2 Related Markets 21
 4 Market Outlook 22
 4.1 Overview 22
 4.2 Regulatory Bodies & Standards 22
 4.3 Porter 5 (Five) Forces 24
 5 Market Characteristics 25
 5.1 Machine Learning Process 25
 5.2 Applications of Machine Learning in Manufacturing 26
 5.3 Market Segmentation 27
 5.4 Market Dynamics 28
 5.4.1 Drivers 29
 5.4.1.1 Rising Importance of Predictive and Preventive Maintenance 29
 5.4.1.2 Increased Adoption of Advanced Analytics in Manufacturing 29
 5.4.1.3 Integration of Machine Learning with Big Data and Other Technologies 29
 5.4.1.4 High Volume of Structured and Unstructured Data 29
 5.4.2 Restraints 31
 5.4.2.1 Implementation Challenges 31
 5.4.2.2 Rigid Business Models 31
 5.4.2.3 Dearth of Skilled Data Scientists 31
 5.4.2.4 Affordability of Organizations 32
 5.4.2.5 Data Security Concerns and Data Inaccessibility 32
 5.4.3 Opportunities 32
 5.4.3.1 Untapped Manufacturing Data 32
 5.4.3.2 Digitization Wave in Manufacturing 32
 5.4.3.3 Increasing Complexities in Manufacturing Processes 32
 5.4.4 DRO – Impact Analysis 34
 6 Trends, Roadmap and Projects 35
 6.1 Market Trends & Impact 35
 6.2 Technology Roadmap 36
 7 Geographic Segmentation: Market Size & Analysis 37
 7.1 Overview 37
 7.2 Machine Learning as a Service in Manufacturing Market by Components 42
 7.3 Machine Learning as a Service in Manufacturing Market by Deployment Mode 44
 7.4 Machine Learning as a Service in Manufacturing Market by End-users 45
 8 Global Generalist 46
 Microsoft Corporation 46
 8.1.1 Overview 46
 8.1.1.1 Business units 47
 8.1.2 Microsoft Corporation in Machine Learning as a Service (MLaaS) 50
 8.1.2.1 Business focus 50
 8.1.2.2 Business strategies 52
 IBM Corporation 53
 8.1.3 Overview 53
 8.1.4 Overview: Snapshot 54
 8.1.5 Business Units 54
 8.1.6 Geographic Revenue 57
 8.1.7 IBM Corporation in Machine Learning as a Service (MLaaS) 58
 8.1.7.1 Business focus 59
 8.1.8 SWOT Analysis 59
 8.1.8.1 Business strategies 59
 Amazon Web Services (Subsidiary of Amazon.com, Inc.) 61
 8.1.9 Overview 61
 8.1.10 Overview: Snapshots 61
 8.1.11 Business Units 62
 8.1.12 Geographic Revenue 64
 8.1.13 Amazon Web Services in Machine Learning as a Service (MLaaS) 65
 8.1.13.1 Business focus 65
 8.1.14 SWOT Analysis 66
 8.1.14.1 Business strategies 67
 Google Inc. (Parent company- Alphabet Inc.) 68
 8.1.15 Overview 68
 8.1.16 Overview: Snapshots 69
 8.1.17 Business Units 69
 8.1.18 Geographic Revenue 72
 8.1.19 Google Inc. in Machine Learning as a Service (MLaaS) 73
 8.1.19.1 Business focus 73
 8.1.20 SWOT Analysis 74
 8.1.20.1 Business strategies 74
 9 Companies to Watch for 75
 BigML, Inc. 75
 9.1.1 Overview 75
 9.1.2 Machine Learning Offering 75
 Sight Machine 76
 9.1.3 Overview 76
 9.1.4 Machine Learning Offering 76
 Eigen Innovations Inc. 77
 9.1.5 Machine Learning Offering 77
 Seldon Technologies Ltd. 78
 9.1.6 Machine Learning Offering 78
 Citrine Informatics Inc. 79
 9.1.7 Machine Learning Offering 79
  Acronyms 80


List Of Tables

 TABLE 1 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY REGIONS, 2016-2022 ($MILLION) 41
 TABLE 2 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY COMPONENTS, 2016-2022 ($MILLION) 42
 TABLE 3 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY DEPLOYMENT MODE, 2016-2022 ($MILLION) 44
 TABLE 4 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET BY END-USERS, 2016-2022 ($MILLION) 45
  


List Of Figures

 CHART 2 RESEARCH METHODOLOGY 17
 CHART 3 PORTERS 5 FORCES ON MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET 24
 CHART 4 STEPS IN MACHINE LEARNING PROCESS 25
 CHART 5 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET SEGMENTATION 27
 CHART 6 MARKET DYNAMICS – DRIVERS, RESTRAINTS & OPPORTUNITIES 28
 CHART 7 DRO - IMPACT ANALYSIS OF MACHINE LEARNING AS A SERVICE IN MANUFACTURING 34
 CHART 8 TECHNOLOGY ROADMAP FOR MACHINE LEARNING AS A SERVICE IN MANUFACTURING 36
 CHART 9 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY GEOGRAPHIES 37
 CHART 10 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN AMERICAS 38
 CHART 11 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN EUROPE 39
 CHART 12 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN APAC 40
 CHART 13 MACHINE LEARNING AS A SERVICE IN MANUFACTURING MARKET IN MEA 41
 CHART 14 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY COMPONENTS ($ MILLION) 42
 CHART 15 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY DEPLOYMENT MODE ($ MILLION) 44
 CHART 16 MACHINE LEARNING AS A SERVICE IN MANUFACTURING BY END-USERS ($ MILLION) 45
 CHART 18 MICROSOFT CORPORATION: SWOT ANALYSIS 51
 
  


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