This guide covers AI infrastructure, from hardware acceleration and model serving to monitoring and security, with tools, patterns, and strategies proven in production.
October 2, 2025
by Vidyasagar (Sarath Chandra) Machupalli FBCS
CORE
Agile teams thrive on speed. Time-boxed decisions apply bounded rationality to avoid analysis paralysis, reduce decision fatigue, and deliver value faster.
Treat your security rules and compliance like tests that run every time you perform Terraform Plan. Learn how Policy-as-Code (PaC) allows you to do that.
This article demonstrates how we can run vLLM on Kubernetes for a centralized LLM serving engine that is production-ready and can be used by multiple applications.
From database bottlenecks to lightning-fast APIs, improve your app’s performance by implementing caching in Spring Boot with Redis and ElastiCache for microservices.
Build real-time, serverless dashboards by streaming events with EventBridge, OpenSearch, WebSockets, eliminating polling and delivering instant updates at scale.
Learn in this guide how we migrated to a GitOps workflow with Helm, OpenShift, and ArgoCD — lessons, pitfalls, and wins from real-world Kubernetes deployments.
PySpark jobs often fail because of bad data, network issues, or logic errors. Sometimes, after hours of processing. Learn how to make your Spark pipelines more reliable.
We explore why product professionals risk sleepwalking into strategic irrelevance by over-trusting AI, relying on flawed metrics, and losing direct customer insight.
Continuous integration and continuous delivery serve different purposes in the development pipeline — optimizing each independently leads to better outcomes.
A deep dive into the importance of data quality and strategies for improvement. We also analyze some real-world examples demonstrating the importance of data quality.
Learn how one-week sprints with vibe coding boost Agile success by enabling faster delivery, reducing AI errors, and improving collaboration across teams.