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Machine Learning - Market Insights

What an expert research says about Machine Learning

Machine Learning is a sub set of Artificial Intelligence (AI), and enables the computers to learn without being explicitly programmed, and enables them to perform the task intelligently. A complex data process is carried out in Machine Learning by learning from data, instead of getting into pre-programmed rules.

Determining and properly understanding the structure and patterns hidden in the Dataset, is one of the main purposes and aims of Machine Learning. It is largely based on the ability of the computers to deeply extract the available data even in the absence of a theory on what the data structure may look like. 

Inception of Machine Learning

Machine Learning term was coined by Arthur Samuel in 1959 at IBM, an American pioneer in the field of computer gaming and artificial intelligence. Experts Insights says, that Machine Learning has experienced mainstream adoption.. In 1990s, the work done on machine learning was shifted from a knowledge driven approach to a data driven approach. In 2002, Torch, a software library of Machine Learning was released. In 2015, Amazon launched their own Machine Learning platform.

Machine Learning Market Segmentation

Different Types of Machine Learning Methods -
Various different types of Machine Learning Methods are

  • Supervised Learning

In the context of machine learning, Supervised Learning is defined as the type of system in which both input and output data’s are labelled for classification to provide to provide a learning basis for future data processing. It provides the learning algorithms with known quantities to support future judgements of the situation.

  • Unsupervised Learning

 The training of an artificial intelligence algorithm using information’s that are neither classified nor labelled and also allows the algorithm to act on that information without any guidance is termed as Unsupervised Learning. With the implementation of Unsupervised Learning, it is very easier to get unleashed data from a computer than the labelled data, which needs manual interventions.

  • Reinforcement Learning

In the context of machine learning, Reinforcement Learning is defined as a technique which enables an agent to learn in an interactive environment by the adoption of trial and error feedback method from their own actions and experiences. Major applications of Reinforcement Learning includes text summarization, dialog agents and others.

End Users of Machine Learning

End User industries of Machine Learning are

  • BFSI
  • Healthcare
  • Government
  • Automotive
  • Education
  • Telecom
  • Retail and E-commerce
  • Others

Machine Learning is mainly focused on the advancement of computer programs. Currently Machine Learning is used in various applications, which includes face detection, image classification, speech recognition, antivirus, genetic, signal diagnosing among others.

In BFSI industry, Machine Learning is used in multiple ways. It helps in increasing the sales & marketing, customer centricity and equity predictions for extended investment opportunities. In BFSI industry, Machine Learning also helps in fraud prevention, risk management, loan underwriting, and algorithmic trading and among others.

In healthcare sector, the application of Machine Learning is increasing day by day, it helps in identifying and diagnosing the diseases and ailments, normally which are hard to diagnose. It is also used in early drug discovery process, medical imaging, personalized medicine, smart health records and among others.

Machine Learning is also helpful for the government to deliver better, cost-effective and customer-friendly services. Application of Machine Learning algorithm helps the government agencies to increase their operational efficiencies by analysing their data sets and making predictions for future events.

Industry experts of automotive industry believe that Machine Learning can help them to achieve marketing function goals and measure their return on investments It comprises of product innovations which are precisely connected with self-driving cars, parking and lane-change assists.

Machine Learning in education sector helps in segmenting the entire process of online education which leads to easy access of the subjects through the deployment of their various integrated software’s. Another way by which Machine Learning can benefit the education sector is its ability to predict a student’s performance and even improve education by grouping students and teachers according to their needs and availability.

Insights from experts say that, Machine Learning is being used in telecom industry to enhance their customer service. Machine Learning in telecom industry plays a vital role in network performance mapping social media data to gain customer insights fraud mitigation, identifying and improving server application amongst others.

Experts from Retail and E-commerce industry have analysed that this industry has grown and improved a lot with the deployment of Machine Learning, as it allows the E-commerce business to create personalized customer experience. It even helps the retailers in reducing customer service issues in order to provide enhanced services and build customer satisfaction.

In E-commerce, end user’s search results can be improved as the customer shops on the website based on their personal preferences and history, with the implementation of Machine Learning.

Other end users of Machine Learning includes manufacturing industry, robotics, transportation, oil and gas among others.

Machine Learning is widely being adopted by the end-user for making informed decisions for achieving the objectives and goals of their businesses, and eases their customer service operations while providing customer-centric services.

The global Machine Learning market was valued at US $ 1.29 billion in 2016 and is anticipated to reach at a value of US $ 39.98 billion by 2025.

Major factors driving the growth of Machine Learning market are technological advancements such as open source library, python library and high growth in data generation.

Unavailability of skilled Machine Learning professionals is one of the major factors restraining the growth of this market.

The adoption of Machine Learning technology by the increasing demand for intelligent business processes and rising adoption of modern business applications and tools is foreseen to create lucrative opportunities for the growth of Machine Learning in the market.

Various challenges faced by the technology experts for the adoption of Machine Learning are the inaccessible data, educate customers on possible applications and the low affordability of organizations, as it requires high capital.

Major players functioning in the Machine Learning market includes

  • Alesco Data
  • Ant Works
  • HireIQ Solutions
  • Knexus Research Corporation
  • Pienso
  • Anaconda
  • Aspen Technology
  • Kim Technologies
  • Microsoft Corporation
  • Intel Corporation
  • Google Inc.
  • HP
  • SAP SE
  • IBM
  • Amazon

Future Insights

McKinsey believes Machine Learning will eliminate approx. 50% of the supply chain predictions error, reduce transportation cost by 10%, and cut administrative expenses by 40% in the future. Machine Learning will also help to minimize food waste and drive unequalled efficiency by eliminating bottlenecks, streamlining inventory management, optimizing production and logistics. According to technology consultants it is predicted that Machine Learning when coupled with big data and healthcare can generate a value of $100billion per year in healthcare and Machine Learning is also been used in preventive healthcare filed in this new era. According to the analysis by industry experts it is believed that Machine Learning has the potential to create an additional value of $2.6T by 2020 in sales and marketing and a value of up to $2.0 T in manufacturing and supply chain planning.

Conclusion

The primary reason for the adoption of Machine Learning platforms is to improve customer experience, and it is being adopted by majority of industry leaders to improve every aspect of their personalization strategies.
 


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