Amazon AWS’s role in my Data Science pursuits

In a matter if two months, I climbed to the top 1% of Kaggle by solving some very interesting problems provided by leading organisations and through applying various Machine learning techniques to Complex data. If you haven’t known about Kaggle, it is a global platform that connects Machine Learning Scientists and Engineers with Organisations that wants to solve their data science problems in the form of  competitions.

While I do have some exposure in the AI related areas several years ago, I am neither a real Data Scientists holding a Phd or a Post-doc researcher or an Industry Veteran working in the field of Analytics except the fact I have been learning and working on some of the connected areas offlate. When I started at Kaggle initially, I quickly realised that Solving complex machine learning problems in its true sense is not for the weak hearted!  and I am one of those in the process of getting stronger over every weekend hacks these days and its been an exciting intellectually rewarding journey!

Several times over these weekend pursuits, I had to run algorithms on machines that required very high capacity and I had to do it the lowest cost. Amazon AWS so far has helped me address both these problems with its high memory XL and spot instances combined with the ability to quickly launch different sets of pre-baked machine learning run times through AWS machine images and Cloud-formation deployment.

In essence AWS is significantly helping me to leap forward in my data science pursuits.



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Cloudshoring is a popular blog on Cloud computing and Big data
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