With a population of over 4.4 billion strong and growing, constituting 40% of the world’s GDP and with a GDP growth rate, double that of the rest of the world, APAC undoubtedly, is fast becoming the hub of world’s economic activities. This region also stands as a major manufacturing hub for the world. Naturally, APAC is leading in its strategy deployment efforts in many new technologies, such as M2M. Experimenting incubators for transportation, smart cities and other businesses involving Internet of “things” are now being carried out in IOT domain from this region. The 150 large TSPs and numerous MVNOs are now fast competing to increase their market share in IOT. We will discuss and try capture this transformative, revolutionary journey of TSPs in IoT market and the various challenges faced by them.
Digital business is shifting from a future strategic vision by IT leaders and digital leaders to providing a true competitive edge. New digital technologies are transforming the way your enterprise business operates, as you begin the balance of delivering better service to your customer, improving efficiency, and cutting costs.
Long ago, back around 2007, we all were introduced to the 3rd Platform, with cloud as its core and solutions that offered anytime, anyplace access to application functionality. The platform was built on the technology pillars of mobile computing, cloud services, big data and analytics and social networking.
Many a times, data scientists and analysts input data and then train a model like logistic regression for classification. Most of the practitioners do not seem to spend enough time on this part of the output and instead focus only on the top level diagnostics consisting of coefficient summary, RMSE or classification matrix and may be the overall measure such as an R2 in the case of a linear regression or a C-Stat or an area under curve (AUC) for logistic regression. While not denying the value of these top level diagnostics, it is also important to check for model fit than just the estimation or classification error. This is when the deviance based diagnostics come handy.