Five blockchain use cases: from property to sustainability

Blockchain use cases have to be identified before the technology can be adopted. Five experts do exactly that across their respective industries Five blockchain use cases: from property to sustainability image

As with any technology, blockchain development has to solve a problem. Industry use cases will help bring the infamous tech into the mainstream.

Blockchain is touted as a game changer technology. But, without industry blockchain use cases, it will never reach its predicted potential.

The blockchain development services have already been proclaimed a potential saviour in industries, such as the supply chain, logistics and finance, or rather a number of use cases have been identified, but — really — it will have an impact on a wide variety of industries, in a number of ways.

Below, five experts explain how blockchain technology will impact their respective industries.

Kubernetes and The Future of Software

While Kubernetes is still not in production at most enterprises, Italy's biggest bank shows the way forward.

Kubernetes has become a standard infrastructure API with a gravitational pull that vendors like Red Hat, Mesosphere (now D2IQ), and Pivotal have been unable to escape. If you're in the business of enabling enterprises to build applications, you've got to support Kubernetes. Period.

For those application-building enterprises, Kubernetes adoption has remained somewhat aspirational, with a 2018 CNCF survey finding 40% of enterprises running Kubernetes in production. Among those 60% not yet on the Kubernetes train you'd expect to find risk-averse banks. As an industry vertical, unlike their hedge fund and trading cousins who constantly push for an edge, traditional banks don't like crossing technology chasms unless they have to. These are people who still run their ATM networks on 30-year-old mainframe technologies.

How AI transform traditional software development

6 Ways AI Transforms How We Develop Software

AI is transforming all business functions, and software development is no exception. Not only can machine learning techniques be used to accelerate the traditional software development lifecycle (SDLC), they present a completely new paradigm for inventing technology.

Traditionally, developing custom software applications requires you to specify in advance exactly what you want the system to do and then hand engineer all of the features of your technology. Encoding many tasks in an explicit way is possible, as computers before the advent of AI were still quite powerful.

There are many tasks and decisions, however, that are far too complex to teach to computers in a rigid, rule-based way. Even an activity as seemingly simple as identifying whether a photo or video on the internet is of a cat is beyond the reach of traditional software development.