Machine learning is a computing technology that enables computers to explore, learn, and modify their analytical functionalities when exposed to new data sets, without being explicitly programmed. It is also used to capture data and consequently run discrete modelers to create patterns for subsequent processing, analysis, and interpretations required for real-time decision making.
The global machine learning market is expected to grow from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. The main driving factors for the market are proliferation in data generation and technological advancement.
In the services segment, the managed service segment is expected to grow at a higher CAGR, whereas professional service segment is expected to be a larger contributor during the forecast period. The managed service is said to be growing faster, as it helps organizations to increase efficiency and save costs for managing on-demand machine learning services. The growth of the professional services segment is mainly governed by the complexity of operations and increasing deployment of machine learning solutions.
In the deployment mode segment, the cloud deployment mode is expected to hold the largest market share and grow at the highest CAGR during the forecast period. Flexibility, automated software updates, disaster recovery through cloud-based backup systems, increased collaboration, monitoring document version control, and data loss prevention with robust cloud storage facilities are some of the crucial benefits that have resulted in the adoption of cloud-based delivery models for machine learning software solutions and services.
In the organization size segment, the large enterprises segment is expected to have the largest market share, whereas the SMEs segment is expected to grow at the highest CAGR during the forecast period. The rapidly emerging and highly active SMEs have increased the adoption of machine learning solutions and services globally, as a result of the growing digitization and increased cyber risks to critical business information and data. Large enterprises have been heavily adopting machine learning to extract the required information from a large amount of data and forecast the outcome of various problems.
In the verticals segment, the Banking, Financial Services, and Insurance (BFSI) vertical is expected to be the highest contributor, whereas the healthcare and life sciences vertical is projected to grow at highest CAGR during the forecast period. Both the verticals generate data in a huge amount every second, and there is accelerated demand for data management technologies such as machine learning and predictive analytics in order to extract business critical insights from this ever-increasing data. The other industry verticals, such as manufacturing, telecommunication, energy and utilities, retail, and government and defense are contributing significantly to the machine learning market. These verticals are also expected to witness significant growth rates during the forecast period due to the increased concerns for managing the complex business processes with improved efficiency and lowering the overall costs.
The global machine learning market has been segmented on the basis of regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is estimated to be the largest revenue-generating region. This is mainly because, in the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. These regions have the most competitive and rapidly changing machine learning market in the world. The APAC region is expected to be the fastest-growing region in the machine learning market. The increased awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the APAC region, has led APAC to become a highly potential market.
The major issue faced by most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.
The major vendors that offer machine learning solutions across the globe are Microsoft Corporation (Washington, US), IBM Corporation (New York, US), SAP SE (Walldorf, Germany), SAS Institute Inc. (North Carolina, US), Google, Inc. (California, US), Amazon Web Services Inc. (Washington, US), Baidu, Inc. (Beijing, China), BigML, Inc. (Oregon, US), Fair Isaac Corporation (FICO) (California, US), Hewlett Packard Enterprise Development LP (HPE) (California, US), Intel Corporation (California, US), KNIME.com AG (Zurich, Switzerland), RapidMiner, Inc. (Massachusetts, US), Angoss Software Corporation (Toronto, Canada), H2O.ai (California, US), Alpine Data (California, US), Domino Data Lab, Inc. (California, US), Dataiku (Paris, France), Luminoso Technologies, Inc. (Massachusetts, US), TrademarkVision (Pennsylvania, US), Fractal Analytics Inc. (New Jersey, US), TIBCO Software Inc. (California, US), Teradata (Ohio, US), Dell Inc. (Texas, US), and Oracle Corporation (California, US).
Vendors who fall into this category receive high scores for most of the evaluation criteria. They have a strong and established product portfolio and a very strong market presence. They provide mature and reputable machine learning solutions. They also have strong business strategies. Microsoft, IBM Corporation, SAP SE, SAS Institute Inc., Google Inc., and Amazon Web Services, Inc. are the vendors who fall into the visionary leaders’ category.
Innovators in the MicroQuadrant are vendors that have demonstrated substantial product innovations as compared to their competitors. They have a much-focused product portfolio. However, they do not have a very strong growth strategy for their overall business. Baidu, Inc., BigML, Inc., FICO, HPE, Intel Corporation, KNIME.com AG, RapidMiner, Inc., Dataiku and Angoss Software Corporation are the vendors who fall into the innovator's category.
They are established vendors with very strong business strategies. However, they are low in their product portfolios. They focus on specific type of technology related to the product. Dell and Oracle Corporation are the vendors who fall into the dynamic differentiators’ category.
They are vendors with niche product offerings and are starting to gain their position in the market. They do not have much strong business strategies, as compared to other established vendors. They might be new entrants in the market and require some more time to get significant traction in the market. H2O.ai, Alpine Data, Domino Data Lab, Inc. Luminoso Technologies, Inc., Skytree Inc., Fractal Analytics Inc., TIBCO Software Inc., and Teradata are the vendors who fall into the emerging companies’ category.
Machine Learning Software Quadrant
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- Breadth and Depth of Product Offerings
- Products/Solutions Offered
- Professional Services (Consulting & Training)
- Managed services (Support & Maintenance)
- Organization Size
- Small and Medium Enterprise (Revenue< 100 Million)
- Large Enterprises (Revenue> 500 Million)
- Product Features and Functionality
- cutomer satisfaction
- Types of Offerings
- Software Tools
- Product Features
- Data visualization and exploration
- Model Evaluation and Interpretation Tools
- Modeling APIs
- Machine Learning Algorithms
- Model assessment and scoring
- APIs for Batch and Real-time Predictions
- Data Transformations
- Focus on Product Innovation
- Product Innovation
- R&D Spend
- New Product Developments
- Product Upgradation
- New Product Launches
- Product Differentiation and Impact on Customer Value
- Customer Feedback Frequency
- Solution Scalability
- Product Quality and Reliability
- Level of Support
- Customer Redressal Mechanism/Program
- Technical Support
- Customer Support
- Sales Support
- Others, please specify
- Pre Sales Support
- Software Requirement Specification (SRS)
- Product Demos
- Proof of Concept
- Dedicated Account Manager (DAM)
- Channel for Delivery of Support Services
- On-Site Support
- Remote Support