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Machine learning is a branch of artificial intelligence that enables machines to learn directly from data, experience, and examples. By permitting computers to execute specific tasks smartly, machine learning allows computers to carry complex processes by learning from examples or data, rather than following pre-programmed rules. Increasing volume of data being generated across industry verticals creates an exhaustive repository for machines to learn from, something that is further backed by rapid strides made in processing power of computers, in turn enhancing the analytical capabilities of machine learning systems.    

Increasing advancements in technology leading to higher accuracy of systems fueling market growth   

People interact with various systems, which are based on machine learning such as recommender systems, voice recognition systems, and image recognition systems. Rapid advancement in technology in image recognition system has increased the accuracy of the system, which has fueled the demand for machine learning in various systems. For instance, in image labeling challenge, the accuracy of machine learning was 72% in 2010 and it reached to 96% in 2015. The ability of machines to process large volumes of data and to use the data for prediction have made the machine learning a key tool in various applications such as BFSI, healthcare etc.   

Integration of machine learning in robotics has fueled growth of the machine learning market

Rampant advancements in robotic industry has created various innovations in robots with the integration of sensor technologies and materials. The advancements in machine learning have increased the capabilities of robots to contribute in applications such as drones and autonomous vehicles. Moreover, the increasing demand for advance robotic system in various verticals such as automotive, electronics, food and beverages, healthcare etc has fueled the market growth. According to International Federation of Robots, in 2016, around 294,000 units of industrial robots were deployed across the globe. For example: In 2016, Fanuc, a Japan-based company, announced development of a robot with deep reinforcement learning technique, which enables the robot to train itself over a very short time duration.

Machine learning Market Taxonomy

On the basis of deployment model, the global machine learning market is segmented into:

  • On-premises
  • Cloud-based

On the basis of application, the global machine learning market is segmented into:

  • Banking, Financial Services, and Insurance
  • Education
  • Energy
  • Healthcare & Pharmaceuticals
  • Manufacturing
  • Public Services
  • Retail
  • Transport & Logistics

Machine learning market is expected to witness rampant growth due to growing healthcare sector in the near future.

Accuracy is one of the major concerns in the healthcare sector. Machine learning have the capabilities to provide more accurate diagnosis and healthcare services, which in turn has augmented demand for machine learning in healthcare sector. For instance, diagnosis of diabetic eye disease requires frequent examination of pictures at the back of an eye by the specialist. The features in the image helps to identify sensitivity of disease, which in turn, indicates fluid leakage and bleeding. Moreover, in 2016, Google has developed a deep learning algorithm, which analyze images and provides training to the system by using a data set of 128,000 images. Thus, the system diagnose the disease with a level of accuracy similar to human ophthalmologists. On similar lines, Google researchers are developing a deep learning algorithm for early diagnosis of skin cancer and breast cancer.  

Key Companies in the Global Machine Learning Market       

Microsoft Corporation, SAP SE, Sas Institute Inc., Amazon Web Services, Inc., Bigml, Inc., Google Inc., Fair Isaac Corporation, Hewlett Packard Enterprise Development Lp, and Intel Corporation are some of the major companies operating in the global machine learning market.


Research Methodology

Coherent Market Insights followsa comprehensive research methodology focused on providing the most precise market analysis. The company leverages a data triangulation model which helps company to gauge the market dynamics and provide accurate estimates. Key components of the research methodologies followed for all our market reports include:

  • Primary Research (Trade Surveys and Experts Interviews)
  • Desk Research
  • Proprietor Data Analytics Model

In addition to this, Coherent Market Insights has access to a wide range of the regional and global reputed paid data bases, which helps the company to figure out the regional and global market trends and dynamics. The company analyses the industry from the 360 Degree Perspective i.e. from the Supply Side and Demand Side which enables us to provide granular details of the entire ecosystem for each study. Finally, a Top-Down approach and Bottom-Up approach is followed to arrive at ultimate research findings.

Data Triangulation Methodology | Coherent Market Insights

Coherent Market Insights desk research is based on a principle set of research techniques:

  • National level desk research: It Includes research analysis of regional players, regional regulatory bodies, regional trade associations, and regional organization.
  • Multinational level desk research: The research team keeps a track of multinational players, global regulatory bodies, global trade associations, and global organization.

Coherent Market Insights has a large amount of in-house repository of industry database. This is leveraged as a burner for initiating a new research study. Key secondary sources include:

  • Governmental bodies, National and international social welfare institutions, and organizations creating economic policies among others.
  • Trade association, National and international media and trade press.
  • Company Annual reports, SEC filings, Corporate Presentations, press release, news, and specification sheet of manufacturers, system integrators, brick and mortar - distributors and retailers, and third party online commerce players.
  • Scientific journals, and other technical magazines and whitepapers.

Market Analysis | Coherent Market Insights

Preliminary Data Mining

The raw data is obtained through the secondary findings, in house repositories, and trade surveys. It is then filtered to ensure that the relevant information including industry dynamics, trends, and outlook is retained for further research process.

Data Standardization

Holistic approach is used to ensure that the granular and uncommon parameters are taken into consideration to ensure accurate results. The information from the paid databases are further combined to the raw data in order to standardize it.

Coherent Statistical model

We arrive at our final research findings through simulation models. Coherent Data Analytics Model is a statistical tool that helps company to forecast market estimates. Few of the parameters considered as a part of the statistical model include:

  • Micro-economic indicators
  • Macro-economic indicators
  • Environmental indicators
  • Socio-political indicators
  • Technology indicators

Data Processing

Once the findings are derived from the statistical model, large volume of data is process to confirm accurate research results. Data analytics and processing tools are adopted to process large chunk of collected informative data. In case, a client customizes the study during the process, the research finding till then are benchmarked, and the process for new research requirement is initiated again.

Data Validation

This is the most crucial stage of the research process. Primary Interviews are conducted to validate the data and analysis. This helps in achieving the following purposes:

  • It provides first-hand information on the market dynamics, outlook, and growth parameters.
  • Industry experts validates the estimates which helps the company to cement the on-going research study.
  • Primary research includes online surveys, face-to face interviews, and telephonic interviews.

The primary research is conducted with the ecosystem players including, but not limited to:

  • Raw Material Suppliers
  • Manufacturers
  • System Integrators
  • Distributors
  • End-users