Selecting the right features that contribute to your model is an art and a science. I call it art because much pain can be saved if you have good domain knowledge. The importance of a feature is intuitively known already though the quantity of influence may not be known. It is, of course, science for all the mathematical rigour it goes through for being a selected feature. Today, we will first understand why is feature selection an important aspect of Machine Learning and the

The Rendezvous architecture proposed by Ted Dunning and Ellen Friedman in their book on Machine Learning Logistics was a wonderful solution I found for a specific architectural problem I was working on. I was looking for a tried and tested design pattern or architectural pattern that helps me run Challenger and Champion models together in a maintainable and supportable way. The rendezvous architecture was significantly more useful in the big data world because you are dealing

Machine Learning by itself is a branch of Artificial Intelligence that has a large variety of algorithms and applications. One of my earlier articles on 'The Machine Learning Landscape" provides a basic mind map of the various algorithms. Big data architectures provide the logical and physical capability to enable high volumes, large variety and high-velocity data to be ingested, processed, stored, managed and accessed. The marriage of these two opens up immense possibilities