DevOps

Environment as Code

From Infrastructure as Code to Environment as Code

Resolve Challenges Scaling IaC By Adarsh Shah Infrastructure as Code (IaC) has made managing infrastructure easier in a lot of ways, but there are many challenges that companies accept as the cost of adopting IaC, especially when scaling. My previous … Read More

Example Environment Setup

Challenges scaling Infrastructure as Code

By Adarsh Shah Notes:1. This article is 1st one in a series of articles on “From Infrastructure as Code to Environment as Code“. This article talks about current challenges scaling Infrastructure as Code and in follow-up articles we will talk about “How … Read More

Platform Engineering: Challenges and Solutions

Based on my experience leading Platform Engineering initiative at various organizations By Adarsh Shah I talked about What Platform Engineering(PE) is, When is it useful, and the Challenges I have seen working with it in my previous article. In this … Read More

Platform Engineering: Using it to Gain Competitive Advantage

What is it, When is it useful, and Challenges with it By Adarsh Shah Caution: Platform is a widely used term to define various types of platforms, so be careful about using it. At one place, we chose not to … Read More

Continuous Delivery for Machine Learning Systems

Deploying Machine Learning Systems to Production safely and quickly in a sustainable way Most of the principles and practices of traditional software development can be applied to Machine Learning(ML), but certain unique ML specific challenges need to be handled differently. … Read More

Infrastructure as Code: Principles, Patterns, and Practices

Deliver Infrastructure and Software running on it Rapidly and Reliably at Scale Notes:1. There is a certain level of Organizational maturity needed to use these Principles, Patterns, and Practices. This article is not focused on the cultural side of things … Read More

Challenges Deploying Machine Learning Models to Production

MLOps: DevOps for Machine Learning Also published in towards data science One of the known truths of the Machine Learning(ML) world is that it takes a lot longer to deploy ML models to production than to develop it. According to the … Read More