All sectors of industries are exploring AI powered avenues with an intent of adjusting according to a scale. Solutions that serve better insights are found to be more in demand by industries and all domains are benefited from the advancements in artificial intelligence. But, access to AI backed sophisticated analytics that enables decision-making for strategies are mostly circumscribed because of inability to scale. So AI solutions with impressive results require systematic scaling so as to build a marketplace for the emergent future.
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Demand for delivery of utility services is growing day by day. Pre-payment and post-payment solutions for the delivery of electricity, or water, or gas as services have transformed the mode of delivery of services with the advent of smart-meters, smart-phones and other IoT advancements. Data storage and analytics that go hand in hand are enabling the service providers to focus deeply on the core business rather than other non-core activities. With AI based insights, service providers gain better decision making ability in their business operations and with the microservices based multitenancy environment, the scaling is strategically managed.
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The process of computationally identifying and categorizing opinions is of great value addition for the product owners, brand managers or entrepreneurs to extract insights especially from social media. This analysis of sentiments helps to understand the scenario better and facilitate managers to take better decisions that leads to strengthen their businesses. But scaling is an important factor to consider as the volume, velocity and variety of social media data play a very important role. This aspect of natural language processing, which involves a lot of computation intensive tasks, are managed using the technique of pipelining. Social Dobby is one such product of Evos that facilitates all the functionalities of sentiment analysis along with handling all technical criticalities.
The discovery, interpretation, and communication of meaningful patterns and applying those towards effective decision making is a real challenge for the data scientists. Machine learning algorithms are well known for such predictive analytics but mostly are computation intensive tasks. Interestingly, this challenge of prediction becomes more difficult to handle when there is a constraint of internet communication and hardware resources. With such an intent to use commonly available hardware resources like normal smart phones, AnaLITE, an android app is developed for forecasting tasks. It basically offers an on-device platform to perform predictions in any field that includes weather to finance to government schemes.