Gurugram, February 2017 /PRNewswire/ – It is commonly accepted that AI technology has reached such a level that it is set to disrupt most industries. Today, existing companies harness the efficiencies and insights provided by these technologies or risk getting over run by nimble competitors. MathLogic with its Centre of Excellence (COE) for ML and AI is on a mission to ensure that companies can embrace the new wave of analytics and stay relevant in the marketplace.

There are a number of ways to look at the benefits of adopting Artificial Intelligence technologies. First and foremost is the matter of efficiency – Machine Learning and Deep Learning models can lead to 20-40% performance improvement over traditional modeling paradigms in a variety of use cases. Secondly, there is huge time compression in terms of getting the same output and reducing the decision making time dramatically (10-100X). The use of these techniques was earlier difficult as huge computational resources were required along with the lack of knowledge in leveraging these techniques. However, the advancement of cheaper cloud computing resources and open source tools like Spark has made it easier for companies to adopt these techniques. MathLogic works with its clients in their journey of becoming smarter and adopting the latest that Data Science has to offer. These technologies can help companies do stuff which was not practical earlier - computer vision, real time optimizations and chat bots.

In their quest for making the latest in Data Science available to customers, MathLogic has been experimenting with a host of tools and techniques and their applicability towards solving common business problems using data. In this journey two things caught their attention:

<< Machine Learning techniques like SVM, RF, GBM, ANN can vastly improve accuracy and coverage of current algorithms used by companies. In various use cases from financial services, telecom and healthcare, MathLogic has seen improvements of 20-40% in algorithm predictive power.
<< Use of open source tools for analysis on distributed data – this has lead to dramatic improvements in execution time (10X-100X). It is making computations which were too big to perform, possible in minutes without the cost of heavy duty software or enterprise grade hardware. Machine Learning techniques have been available for several years. However, training model on these techniques remained difficult due to huge computational resources required. It has all changed in the last couple of years with emergence of cheaper cloud computing resources and distributed open source tools like Spark.