When Smart Apps got you thinking……!!

Bright, When Dark…. Nobody Home?!…

These are neither titles of any upcoming Hollywood blockbuster nor are they names of any book. They are names of smart apps (intelligent apps), though!

Business applications have evolved very significantly in the past decade, from web to mobile to multi-channel apps. We are now at another inflection point in the history of application development. The rapid rise of Internet of Things (IoT), big data and machine learning are radically redefining apps as we know them. We are in an era of Smart Apps (Intelligent Apps).

Smart apps are intelligent, contextual and proactive. They make your life, in any sphere, convenient and connected through one device, a smart phone. Whether it’s an elaborate hack or a simple everyday use, intelligent apps have made their presence everywhere.

The evolution of intelligent products—which are increasingly embedded in broader systems—is radically reshaping companies and competition.

Compassites is an upcoming leader in this field of smart apps on IoT. With its experienced team harnessing the right technology stack, Compassites has created innovative smart apps which can create a difference in everyday life.

Smart thermostats controlling a growing array of home devices, transmitting data about their use back to manufacturers; intelligent, networked industrial machines autonomously coordinating and optimizing work; cars streaming data about their operations, location, and environment to their makers and receiving software upgrades that enhance their performance or head off problems before they occur, are a few examples of the smart product-smart app yoke. The relationship a firm has with its products and with its customers is becoming continuous and open-ended.

To exploit the capabilities of smart products, we must first understand their inherent components, technology, and capabilities.

The entire range of smart appliances to industrial equipment, share three components: physical (mechanical and electrical parts); smart (sensors, microprocessors, data storage, controls, software, an embedded operating system, and a digital user interface); and connectivity (ports, antennae, protocols, and networks that enable communication between the product and the product cloud).

There has been a veritable surge in the number of tools and components needed for intelligent application development, with most of it being available as open source. This unprecedented accessibility of application development tools and components has altered the very nature of applications. They have become more intelligent, elastic, highly available, dynamically scalable in regards to load, and have sophisticated and heterogeneous user interfaces.

Smart apps also demand a change in the development approach.

Rather than writing custom apps from scratch, app developers need to focus two things:

  1. Collecting appropriate infrastructure elements as building blocks for the application
  2. Writing the specific business intelligence for the product

The age of using smart hardware and infrastructure as a panacea to solve the inadequacies of applications is fading into the sunset. The trend of building smart applications from multiple distributed components and services running on commodity hardware and public clouds, is fast catching up. Services, including security services built to work with these applications must respond to these trends to be future proof.

Machine learning, along with artificial intelligence and cognitive systems, is gaining popularity. The day may not be too far away, when we have its colossal impact on the software industry.

 This discipline is based on a simple (not simplistic though) principle:

LEARNING=REPRESENTATION+EVALUATION+OPTIMIZATION.

This is being increasingly adopted with many analytics, Big Data and business intelligence initiatives, laying side-by-side with other analytics solution.

When open  source machine learning is coupled with deep learning frameworks, running in cloud(e.g. Scikit-LearnNLTK, Numpy, Caffe, TensorFlow, Theano, or Torch companies), companies will be easily able to leverage pre-trained hosted models to tag images, recommend products  and do natural language processing tasks.

Smart apps that seamlessly leverage big data and analytics are key to global business initiatives building around intelligent apps.

As investors think about the market dynamics of major industry shifts, it is certain that the rise of intelligent apps will create a world of opportunities for start-ups and large technology companies alike.

The use of high-quality, relevant data in machine learning models give rise to use-case specific or industry-specific applications. These are the most immediate nuggets of opportunity at the Finished Services or application layer. Today, the main categories of use-case specific applications are security and anomaly detection, sales and marketing optimization, autonomous systems and personal assistants. There is also a rise in some vertically focused intelligent apps serving retail, healthcare, agriculture, financial services and biotech services.

With companies adopting the microservices development framework, the ability to plug and play different machine learning models and services to deliver specific functionality comes into practice. The two categories of companies that we see are: the providers of raw machine intelligence (core building blocks for developers to build intelligent apps) and the providers of trained models or “Models as a Service.”

Collectively, smart apps have amplified the productivity of their owners, allowed them to dive and delve deep into a world of entertainment and convenience, and have got business thinking of turning any app into an intelligent app.

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