Big Data, Cloud & DevOps

Companies across sectors are already collecting data at such a scale that big data has become a buzzword technology. They want lots of it because they want an edge wherever they can get it.  Who wouldn’t? Data increases the quality of decisions made on lower rungs of the ladder. Those decisions end up being more often data-informed, and data is power. But that’s easier said than done lately. Businesses have no issue collecting data nowadays, but they do tend to keep it cordoned off.

It is hard to imagine a Board member today in any large organisation who would be unaware of cyber threats. Of course, priorities may vary in line with economic conditions or the general health of the business, but “cyber” is on the agenda of all Boards, and consistently rated as a top risk by many. The focus of the Board has shifted towards execution, very often in exchange of significant investments in cyber security, in particular where initial maturity levels were low.

As cognitive technologies and automation force every job to be reinvented or adjusted, the future of work is rapidly developing around the idea of an ‘augmented workforce’ made more capable by technology. The future of work is defined by high-tech, smart management, and even smarter employees. Tomorrow’s successful companies are embracing automation, taking better care of their employees, and restructuring offices into healthy, open, and collaborative spaces. They’re increasingly relying on the gig economy, and leveraging the quantified self to improve wellness and motivation.

 It’s time to take stock and the picture is somewhat bleak. Instead of a global technological utopia, there are a number of worrying signs ranging from income inequality to the rise of popular authoritarianism. The fact is that technology and globalization have failed us. Technology alone will not save us. To solve complex challenges like inequality, climate change and the rise of authoritarianism we need to take a complex, network based approach. We need to build ecosystems of talent, technology and information. That won’t happen by itself, we have to make better choices.

Multi-channel design simply means that a channel is being used multiple times. The channel is only needed to be drawn once, and placed into the design process. Are you using a multi-channel design tool? What are your top benefits? Multi-channel design is something supported in multiple stages in the overall design process. Multi-channel design is a must for future design projects and there are certainly many more benefits to implementing a multi-channel design tool within your PCB design process.

The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. This article focuses on the importance of visualization with data. The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.

How Decision Trees work exactly? This is one of the most asked questions in ML/DS interviews. We generally know they work in a stepwise manner and have a tree structure where we split a node using some feature on some criterion. But how do these features get selected and how a particular threshold or value gets chosen for a feature? This post will talk about three of the main splitting criteria used in Decision trees and why they work. 

Have you ever thought what will be in the future with your experience and skills that you are getting during your lifetime? What if people from around the world can create the skills for enterprise software or IT products/programs and publish them on the marketplace? It means any person, or a developer, or a consultant can transfer their knowledge by creating digital skills, and publish them on the marketplace (skills store), specify the pricing for the skills and sell them. 

For many CIOs who are expected to lead the charge towards the digital holy grail, the exercise can be a hair-raising challenge that requires them to navigate an unchartered IT minefield, with redundant and obsolete applications that have accumulated over the years threatening to blow the entire exercise out of the water. It’s essential that before embarking on a digital transformation journey, CIOs ensure they fully understand and then streamline the organisation’s application portfolio.

There is clearly no other way to succeed in devops than to maximize the automation of your entire release processes, from coding through production. At the heart of the maturity lies a continuous improvement mindset within the organization. Such a mindset should build, over time, a foundation of tools that fit the processes and people with their respective skillsets. The market doesn’t stand still; it continues to innovate around machine learning, AI, and software delivery automation capabilities.

Python is an interpreted, high-level, general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace.  In this article, you will learn how to add and configure Black, pytest, Travis CI, Coveralls, and PyUp. We’ve set the stage for more secure code with more consistent style. Here’s our ten-step plan for this article. This guide is for macOS with Python 3.7. Everything works as of early 2019, but things change fast.