Coding

Coding

 

As all areas of business move into digital technology, Ramesh Subramanian explains why the digital transformation requires infrastructure engineers to expand their software skills. 

Several years ago I started my career as a C++ programmer but to be relevant as a software engineer today would require many more software engineering skills. The same logic holds for Infrastructure engineers.

About 94% of enterprises (and 50% of Governments) use some form of cloud (private/public) today. And as per an estimate from Forbes, 83% of workloads will be in the cloud by the end of this year. These stats imply that infrastructure teams should be ready to:

Meet expectations of faster release cycles, with several releases per day becoming the norm

Provide application access at scale (millions of concurrent sessions, potentially across the globe) with 24×7 availability

However, only 30% of all infrastructure teams are using DevOp. This is even lower at 12% for Financial Services firms.

 

Points covered in this article include:

  • Business enabling applications
  • Business critical applications
  • Infrastructure team training

 

Read the full article, Why Infrastructure engineers should start thinking like software developers, on LinkedIn.

 

 

Davide Gronchi provides two simple tools that can help collect answers to powerful questions.

Advanced analytics and machine learning are some of the ready-to-use technologies that help discover correlations and drive conclusions out of complex data sets that often describe our business and production processes. This is very helpful to take decisions aiming to prevent something unwanted to happen e.g., set process parameter to X in order to obtain product spec within tolerance.

There are many other opportunities to eliminate “waste” out of business processes that don’t require complex tools and data scientist skills but “just” common sense and good leadership. Solving problems should always start with a clear definition of “what is the problem?” Often we mix up the symptoms with the root causes, by doing so we look for solutions to the symptoms but don’t eliminate the root cause. Guess what? The problem will be back very soon…

Following a structured problem-solving approach is not difficult but requires discipline and asking the right questions, what we call “powerful questions“. These are questions that make people thinking, typically open questions that require an articulated answer, not just a binary yes/no.

Asking powerful questions should be one of the core skills of good leaders: not solving problems themselves but helping their teams to do so. I believe many have forgotten this and risk to lead teams in endless problem solving rounds without sustainable and substantial results.

 

Included in this article:

  • Fishbone diagram
  • Pareto chart

 

Read the full article, The Simple Art of Problem Solving, on the Growing Operations Advisors website.

 

 

This article from Karthik Rajagopalan’s company blog introduces a type of machine learning that may provide solutions for some traditional optimization problems such as inventory optimization and supply chain optimization.

The field of artificial intelligence is slowly beginning to permeate our lives. Computers and other machines are being endowed with intelligence through a process called machine learning. One particular type of machine learning that is of interest to us here in Paramis Digital is Reinforcement Learning (RL), which has been quite successful in a limited number of applications like teaching computers how to play video games and teaching robots how to perform certain activities.

Supervised learning uses data with input and labeled output and learns the relationship between them. Unsupervised learning, on the other hand, works on unlabeled data and is generally used for pattern detection. It is not uncommon to find applications that use a combination of the two methods. In reinforcement learning an agent, say a robot, learns to perform activities in an environment to accomplish a specified goal. The agent learns by performing various activities, collecting feedback from the environment in response to the activities and evaluating the feedback. The key part of this learning method is the reward mechanism, a mathematical construct, which rewards the agent for performing activities that will move it towards and eventually accomplish the goal. The agent is also punished for performing activities that detract it from accomplishing its goals.

 

Read the full article, Reinforcement Learning, on the Paramis Digital website.

 

Umbrex is pleased to welcome Justin Lechner with Bonsai Growth. Justin Lechner is an ex-Bain consultant based in Brooklyn, NY. After leaving Bain, Justin has split his time between ventures in the enterprise AI space and independent consulting work (innovation and technology projects).

Justin’s particularly passionate about applications of data science within the Consumer, Healthcare, and PE spaces. Justin is excited to collaborate on projects with a data focus

 

Ben Dattner co wrote this article for Harvard Business Review on the issue of building ethical AI for talent management. 

Artificial intelligence has disrupted every area of our lives — from the curated shopping experiences we’ve come to expect from companies like Amazon and Alibaba to the personalized recommendations that channels like YouTube and Netflix use to market their latest content. But, when it comes to the workplace, in many ways, AI is still in its infancy. This is particularly true when we consider the ways it is beginning to change talent management. To use a familiar analogy: AI at work is in the dial-up mode. The 5G WiFi phase has yet to arrive, but we have no doubt that it will.

 

Areas covered in this article include:

  • Training data sets
  • Efficient predictions on a candidate
  • Bias and creating homogeneity in organizations

 

Read the full article, Building Ethical AI for Talent Management, on the Harvard Business Review.

 

 

Tobias Baer provides clear and concise examples of how Google uses the acquisition of select data to create bias, which leads to the dissemination of inaccurate information.  

 

I’m an avid user of the navigation function of Google Maps. Every time I reach my destination, Google asks me for feedback on the navigation instructions. What could possibly be wrong with that? Well, I bet that the data and any analytics derived from that feedback often – and, vastly! – overestimates users’ satisfaction. Why is that?

The app is a perfect illustration of availability bias. I only am given this opportunity to provide feedback when I reach my destination. Which means that if I reach a river only to find that the ferry supposed to take me and my car to the other riverside has stopped operations an hour ago, or if after a few hours of cycling I find that the path indicated by the app leads straight into a gigantic military infrastructure that is fenced by barbed wires with large red signs threatening any trespasser to be shot (both has actually happened to me), and hence my only option is to abolish my route, exit the navigation, and go back to where I come from, no feedback is collected.

 

Points covered in this article include:

  • The problem with creating algorithms quickly
  • The lack of sufficient communication
  • The challenge of creating objective, systematic assessment procedures

 

Read the full article, A Little Example How Google Creates Biases, on LinkedIn.

Karthik Rajagopalan’s company blog explains how machine learning models can facilitate a deeper understanding of the drivers of churn, leading to better solutions that can help customer retention for subscription businesses.

Subscriptions have been around for a very long time. Having come a long way from the hire-for-purchase model introduced by the Singer sewing machine company, the past few decades saw the emergence of memberships in retail, health clubs, and monthly subscriptions to services like telephone and cable television. More recently, the recurring revenue model has been pushed forward by e-commerce and software-as-a-service (SaaS) businesses with the introduction of services for streaming media, connected home, connected car, gaming, etc. According to the Subscription economy index (SEI), the subscription economy has grown by nearly 300% over the last 7 years and is also increasingly correlated with traditional economy, a significant yet not so surprising development.

 

Topics covered in this article include:

-Churn model

-Churn rate

-Customer longterm value

-Promotion campaigns

 

Read the full article, AI augmented retention program is a must for subscription businesses, on the Paramis Digital website.