The more we travel in time, towards future, cloud computing keeps on captivating us with its mysterious enticing.The competition is heating up in the public cloud space as vendors regularly drop prices and offer new features.I will shine a light on the competition between the three giants of the cloud:Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft’s Azure.   Continue reading

One-Click Deployment with AWS CodeDeploy

AWS CodeDeploy is a deployment system that enables developers to automate the deployment of applications on EC2 instances  and to update the applications as required.

You can deploy a nearly unlimited variety of application content, such as code, web and configuration files, executables, packages, scripts, multimedia files, and so on. AWS CodeDeploy can deploy application content stored in Amazon S3 buckets, GitHub repositories, or Bitbucket repositories. You do not need to make changes to your existing code before you can use AWS CodeDeploy. Continue reading

Schedule Daily EC2 instance stop using CloudWatch Events


Infra/Dev-ops team do have instance created for POC/Demo/testing purpose which we need to stop daily (office off-hours) or during weekends for cost saving purpose. As this adds an overhead for us to daily stop the instance manually before leaving office and some times we might forget to stop the instance which again will add up the cost.So there was a demand to automate this process in order to save cost. Continue reading

How to communicate with an OPD Partner?

Having spent 14+yrs working exclusively with start-ups and being a part of product development life cycles for over 30 startup products, I have observed reluctance from founders to outsource the core engineering part of their product. When I tried to dig out the reasons behind their reluctance most feedbacks pointed towards communication overhead, pace of the development and deviation from the expected outcome. But when I analysed further, I realised that those were only the consequences of the communication gap which in turn is a primary concern.  Continue reading

Handling Categorical Features in Machine Learning

Introduction: Every dataset has two type of variables Continuous(Numerical) and Categorical. Regression based algorithms use continuous and categorical features to build the models. You can’t fit categorical variables into a regression equation in their raw form in most of the ML Libraries. If it is not included in the modeling, then you do not get an accurate model. It’s crucial to learn the methods of dealing with such variables. There are many machine learning libraries that deal with categorical variables in various ways. Approach on how to transform and use those efficiently in model training, varies based on multiple conditions, including the algorithm being used, as well as the relation between the response variable and the categorical variable(s). Here I take the opportunity to demonstrate the various methods prevalent and incorporated in the popular Machine Learning Library in Spark, i.e.Mllib for handling categorical variables. Continue reading