Global Machine Learning as a Service in Manufacturing Market: By Components (Software Tools, cloud and web-based application programming interface (APIs) and Others), By Deployment Mode (Public and Private), By End-Users (Process Industries and Discrete Industries) and By Regions (Americas, Europe, APAC and MEA): Global Drivers, Restraints, Opportunities, Trends, and Forecasts to 2023
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.
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.
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.
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.
1.1 Industry Overview
1.2 Industry Trends
1.3 Pest Analysis
2 Report Outline
2.1 Report Scope
2.2 Report Summary
2.3 Research Methodology
2.4 Report Assumptions
3 Market Snapshot
3.1 Total Addressable Market (TAM)
3.2 Related Markets
4 Market Outlook
4.2 Regulatory Bodies & Standards
4.3 Porter 5 (Five) Forces
5 Market Characteristics
5.1 Machine Learning Process
5.2 Applications of Machine Learning in Manufacturing
5.3 Market Segmentation
5.4 Market Dynamics
184.108.40.206 Rising Importance of Predictive and Preventive Maintenance
220.127.116.11 Increased Adoption of Advanced Analytics in Manufacturing
18.104.22.168 Integration of Machine Learning with Big Data and Other Technologies
22.214.171.124 High Volume of Structured and Unstructured Data
126.96.36.199 Implementation Challenges
188.8.131.52 Rigid Business Models
184.108.40.206 Dearth of Skilled Data Scientists
220.127.116.11 Affordability of Organizations
18.104.22.168 Data Security Concerns and Data Inaccessibility
22.214.171.124 Untapped Manufacturing Data
126.96.36.199 Digitization Wave in Manufacturing
188.8.131.52 Increasing Complexities in Manufacturing Processes
5.4.4 DRO – Impact Analysis
6 Trends, Roadmap and Projects
6.1 Market Trends & Impact
6.2 Technology Roadmap
7 Geographic Segmentation: Market Size & Analysis
7.2 Machine Learning as a Service in Manufacturing Market by Components
7.3 Machine Learning as a Service in Manufacturing Market by Deployment Mode
7.4 Machine Learning as a Service in Manufacturing Market by End-users
8 Global Generalist
184.108.40.206 Business units
8.1.2 Microsoft Corporation in Machine Learning as a Service (MLaaS)
220.127.116.11 Business focus
18.104.22.168 Business strategies
8.1.4 Overview: Snapshot
8.1.5 Business Units
8.1.6 Geographic Revenue
8.1.7 IBM Corporation in Machine Learning as a Service (MLaaS)
22.214.171.124 Business focus
8.1.8 SWOT Analysis
126.96.36.199 Business strategies
Amazon Web Services (Subsidiary of Amazon.com, Inc.)
8.1.10 Overview: Snapshots
8.1.11 Business Units
8.1.12 Geographic Revenue
8.1.13 Amazon Web Services in Machine Learning as a Service (MLaaS)
188.8.131.52 Business focus
8.1.14 SWOT Analysis
184.108.40.206 Business strategies
Google Inc. (Parent company- Alphabet Inc.)
8.1.16 Overview: Snapshots
8.1.17 Business Units
8.1.18 Geographic Revenue
8.1.19 Google Inc. in Machine Learning as a Service (MLaaS)
220.127.116.11 Business focus
8.1.20 SWOT Analysis
18.104.22.168 Business strategies
9 Companies to Watch for
9.1.2 Machine Learning Offering
9.1.4 Machine Learning Offering
Eigen Innovations Inc.
9.1.5 Machine Learning Offering
Seldon Technologies Ltd.
9.1.6 Machine Learning Offering
Citrine Informatics Inc.
9.1.7 Machine Learning Offering
Infoholic Research works on a holistic 360° approach in order to deliver high quality, validated and reliable information in our market reports. The Market estimation and forecasting involves following steps:
- Data Collation (Primary & Secondary)
- In-house Estimation (Based on proprietary data bases and Models)
- Market Triangulation
Market related information is congregated from both primary and secondary sources.
Involved participants from all global stakeholders such as Solution providers, service providers, Industry associations, thought leaders etc. across levels such as CXOs, VPs and managers. Plus, our in-house industry experts having decades of industry experience contribute their consulting and advisory services.
Include public sources such as regulatory frameworks, government IT spending, government demographic indicators, industry association statistics, and company publications along with paid sources such as Factiva, OneSource, Bloomberg among others.