7 Most Common Big Data Blunders Every Business Should Avoid
As more companies invest in big data and analytics, there is growing confusion around the best data practices and how businesses should leverage their new investment. While it is essential for businesses to understand how to maximize big data’s potential, it’s equally as important to know... Read more
Is Groovy a Viable Language for Data Science Applications? 5 Pros and Cons
Choosing the right programming language can make a remarkable difference in data science applications. While the industry standards are Python and R, some data scientists have branched off to use others they prefer. One such possible alternative is the Groovy programming language. Apache Groovy is an... Read more
6 Most Common Errors When Implementing AI and Machine Learning
Artificial intelligence and machine learning are steadily rising in popularity, but how can organizations and businesses avoid errors when implementing AI? New technology can be challenging to navigate at first, especially for organizations that aren’t digitally dextrous to begin with. Artificial intelligence (AI) and its partner... Read more
Git vs SVN: What’s the Difference?
Managing the source code is one of the key factors in any development environment. Version control systems or VCS came into prominence to offer an effective solution to the code management needs while facilitating a version-controlled multi-user environment. With the growing popularity of practices like Infrastructure... Read more
Computer Vision & Data Annotation – An Easy Way of Understanding the Relevance in the Real World
Here, you will find a brief explanation of computer vision, some cases we are experiencing in real life, and some of the existent techniques in data annotation supporting the advance of computer vision. I want to highlight upfront that I’m not approaching any computer vision algorithms... Read more
Seven Questions to Ask Before Implementing AI in Your Enterprise
Artificial intelligence is the talk of the digital town, and probably will be for many more years to come. Due to the surge in AI’s popularity across several industries, many businesses are eagerly investing in this technology. While artificial intelligence is undoubtedly transforming the way we... Read more
Data science teams are multidisciplinary, each with different skills and technologies of choice. Some of them use SAS, others may have analytical assets already built in Python or R. Let’s just say each team is unique. As part of our Continuous Integration/Continuous Delivery with monthly releases,... Read more
How to Create a Kubernetes Cluster Using Minikube
Using Kubernetes, we can handle a cluster of servers as one big logical server that runs our containers. We declare a desired state for the Kubernetes cluster, and it ensures that the actual state is the same as the desired state at all times, provided that... Read more
Understanding the “Machine Learning Way” to Solve Business Problems through Real-World Scenarios 
Ironically, one of the foremost barriers preventing the exploitation of machine learning in a business is neither the implementation of the algorithm nor the retrieval of the data (the how): the toughest part is to recognize the right occasion to use it (the why)! We need to... Read more
Systems built with software can be fragile. While the software is highly predictable, the runtime context can provide unexpected inputs and situations. Devices fail, networks are unreliable, mere anarchy is loosed on our application. We need to have a way to work around the spectrum of... Read more