Big Data, Cloud & DevOps

APIs (Application programming interfaces) have been a game-changer. Though APIs have been around for decades, but during the past decade, this technology has become a major force. APIs create powerful ways for businesses to streamline how they engage with partners and together deliver new generations of applications that empower consumers with more services and options. APIs enable companies to more easily build products and services that would otherwise take too long to build. The prospects for the API economy are exponential.

Dynamic Programming forms the basis of some of the most asked questions in Data Science/Machine Learning job interviews, and a good understanding of these might help you land your dream job. In Dynamic programming, we take a bottom-up approach. It is essentially a way to write recursion iteratively. Recursion is a mathematical concept and it comes naturally to us. This post is about study and explaining Dynamic Programming concepts for the data scientists in an easy to understand way.

This post explains how Sequence works in SQL Server that has started to be used since the 2012 version. First, SEQUENCE is a separate object and is not bound to a specific table and one SEQUENCE object can be used on multiple tables in parallel. Second, SEQUENCE can be altered. Then the SEQUENCE object can define the rank etc  The SEQUENCE object is most commonly used to obtain the value for a surrogate key in tables, most commonly used in the same way as IDENTITY is used.  

A transformational process that lacks considering the human element is setting the organization up for a massive downfall. You don’t want to put your company in the situation where they are enabled with the right tools just to get frustrated on how to go about using them to their full potential. With the hundreds of promising software solutions flooding the market at an unprecedented rate, it’s a mystery why any organization would have trouble going completely digital,

The transformation of Manufacturing goes beyond the confines of intelligent factories. With smart products, Customers and Business are increasingly connected. This connectivity includes of course the Internet of Things (IoT). But it also involves Artificial Intelligence (AI) as well as end-to-end Value Stream digitization and automation. New business models are emerging and transforming – even disrupting – age old Manufacturer-Distributor-Customer Relationships. Connectivity with AI, evolved CRM, and end-to-end Value streams are enabling new opportunities for Innovation – involving the Customer with ever increasing demands for Personalization.

How to speed up IT cultures? Speed really is at the heart of the pursuit of intuitive technologies like AI and machine learning. It’s also why IT leaders should not only be seeking out these tools but creating a culture of speed around them. A culture of speed means working your teams to the bone in order to meet unreasonable deadlines. Here are seven key steps for building that culture of speed in your IT organization.

This post looks at the requirements development process and its outputs on a real-world project. There is an approach for developing requirements that is easy enough to implement and better at delivering value to users and stakeholders. The ROI on the initial investment of time and effort is material and the payback period can be as short as a single sprint, so it is definitely worth doing. Use a different toolchain to manage requirements. 

We must try to adapt to our surroundings, as the world keeps changing. So we must follow the new ways of the world to survive in our environment. Agile helps us to do that. It means that we should develop new techniques to make ourselves grow stronger and better. There is no perfect methodology, and there is no ‘best practice.’ We have to learn from each other by teaming up, but if this way doesn’t work, don’t pair at all. Try using Agile on the company to see if it works.

This is all about developing software in healthcare. Why are requirements important? Because poorly managed requirements result in confusion, waste and failure and it’s where countless software projects go wrong almost as soon as they’ve started. This post outlines different views of requirements – objective, ideal, real world, and constructive. The real world view describes the challenges, and the constructive view a rough model developed to deal with them that you can use for your own requirements.

With a solution to almost every machine learning problem, Amazon Machine Learning offers a rich set of tools for machine learning engineers to work with. Amazon also adds new services every few months based on new use cases, making it one of the most dependable platforms for engineers to build AI solutions for their customers. In this article, we will take a look at each one of the machine learning tools offered by AWS and understand the type of problems they try to solve for their customers.

As senior executives turn a page and we enter – possibly – an execution-dominated decade around cyber security, many CISOs are just not equipped to lead. The profile of the chief information security officer  (CISO) needs to change to adjust to the imperatives of the “when-not-if” era:  It becomes essential to start prioritising leadership skills over technical skills and distribute roles across a structured function, instead of looking for “unicorn” profiles: Nobody can be credible on all fronts across all functions and geographies of the business. Those profiles don’t exist and pretending otherwise is just setting the CISO to fail.