Subeesh Sivanandan

In the rapidly evolving world of artificial intelligence and machine learning, deploying models efficiently and reliably is just as crucial as building them. This is where MLOps—a blend of Machine Learning and DevOps—steps in to bridge the gap between data science and IT operations. But how do organizations implement MLOps effectively? What tools and processes

Introduction Kubernetes has emerged as the de facto standard for container orchestration. However, managing complex Kubernetes deployments can be daunting, especially when dealing with multiple microservices, configurations, and environments. This is where Helm charts come into play — offering a powerful way to package, configure, and deploy Kubernetes applications with ease and consistency. In this

Securing your Linux production servers is not just a technical necessity but a business imperative. Cyber threats evolve rapidly, and a single vulnerability can lead to costly breaches, downtime, or data loss. This blog post provides an expert-level, comprehensive checklist to harden your Linux servers, covering firewall rules, user permissions, SSH configurations, CVE management, OS

Delivering high-quality applications quickly is non-negotiable. Continuous Testing (CT) integrated within Continuous Integration/Continuous Delivery (CI/CD) pipelines is the secret sauce that helps teams catch defects early, accelerate release cycles, and ensure software reliability. But how exactly do you set up continuous testing effectively? What tools and strategies work best across different programming languages? And how

Continuous integration and continuous deployment (CI/CD) pipelines have become the backbone of delivering high-quality applications quickly and reliably. But while application code often flows smoothly through automated pipelines, database schema changes and migrations frequently remain manual, error-prone bottlenecks. This disconnect can cause deployment delays, data inconsistencies, and costly rollbacks. This blog post dives deep into

Let’s be honest—legacy systems are exhausting. If you have ever spent hours firefighting broken deployments, chasing dependencies across environments, or waiting days (sometimes weeks) for a release to go live… you know exactly what I’m talking about. For years, I watched teams I worked with struggle to ship confidently. And despite having brilliant engineers, they

Organizations rely heavily on DevOps to accelerate software delivery while maintaining stability and quality. But how do you truly measure DevOps success? The answer lies in metrics that not only track engineering performance but also connect directly to business outcomes. This blog dives deep into the most important DevOps metrics, especially the renowned DORA metrics,