Machine Learning

  • Artificial Intelligence based Decision Support.
     
  • Descriptive, Predictive, and Prescriptive Machine Learning.
  • Innovative Data Modeling and Analysis.
  • Objective data-driven decision making.

Machine learning (ML) is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

ML decision support solutions are capable of analyzing vast quantities of complex data with sophisticated computer algorithms to detect patterns in behaviour and consequently making calculated judgment calls. Although cognitive ML decision support technologies can generally deliver faster more accurate results, it can time and significant resources to get the technology to a point where it is able consistently identify profitable opportunities and potential risks.

​Businesses are accumulating massive volumes of data through their operations. The smarter organizations are aware of the potential opportunities the collected data offers them to gain a competitive edge over rivals. However the sheer volume and disparate nature of the data is too complex for human decision makers to decipher into useful information.  The availability of powerful cloud hosted technical infrastructure has made data intensive ML business intelligence solutions are viable opportunity for organizations to pursue.

Accessibility to complete, relevant, and high quality data is the fundamental necessity for successfully delivering ML decision support solutions. No matter how sophisticated the ML algorithm, the results will only be as good as the data with which it is trained. Inaccurate poor quality data results in unreliable ML decision making.

Organizations with mature data governance practices are better prepared for successfully introducing ML decision making capabilities. They will be aware of the available data sources, as well as the scope and quality within each of them. Providing stakeholders with sufficient transparency, to allow them to make any necessary enhancements to data sources to ensure the success of the ML solution.  

ML solutions are categorized as either Supervised, Unsupervised, or Reinforced learning technologies. Although each type has its own unique way of learning, they do share common characteristics. ML solutions (decision maker) involve implementing an algorithm (environment) that utilizes a ‘feedback loop’ to access and improve the on-going accuracy of its actions (decisions). All ML solutions require continual training and testing over their life-time to monitor and maintain their decision making capabilities.

​​​Supervised ML solutions are designed to respond to a specific task. Data is provided one at a time in the form of ‘data and labels’. Humans evaluate and update the ‘feedback loop’ with details upon the accuracy of the response. The eventual goal being for ML/AI solution to be able to independently predict the correct outcome based upon data that it hasn’t experienced before.  ​

​​Unsupervised ML solutions are developed without direct human engagement. They are provisioned with potentially multi-terabytes of ‘unlabeled’ data, and the tools to understand the properties of the data. ML technology discovered patterns are published so that are understandable by humans and consumable by other technologies.  ​​

Reinforced ML solutions develop by learning from their own mistakes. A similar concept to the way a human brain develops. The algorithm receives signals via the 'feedback loop' to distinguish between positive and negative behaviour. The algorithm is reinforced to prefer good over bad results. A reinforced ML/AI business intelligence solution will initially begin by making many mistakes. As it matures over time, it learns to make fewer mistakes. Eventually evolving into making accurate judgment calls within highly complex decision making environments.

ML technologies have been successfully implemented in a range of decision making situations. They include: proposing products to customers based upon their historical purchasing habits, identifying previously untapped customer segments to explore, and recommending financial investment opportunities. 

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