Learn how to benchmark LLM inference using NVIDIA GenAI-Perf and vLLM on GPU infrastructure. This guide walks developers and platform teams through setting up a single-node inference stack, measuring latency and token throughput, understanding KV cache and batching behavior, and building a Grafana-based observability pipeline. Ideal for engineers learning LLM inferencing and teams operating production-grade inference platforms.

As artificial intelligence continues to reshape industries, the foundational technologies enabling its deployment—cloud infrastructure...

In this hands-on guide, we explore how to automate Databricks deployments using Terraform and GitHub Actions—covering everything from workspace provisioning on Azure to deploying and scheduling Spark jobs via code. Perfect for data engineers and DevOps teams, this blog shares real-world code, architecture, and CI/CD workflows to help you build reproducible, scalable data pipelines using modern DataOps practices.

Software development is evolving rapidly, and Artificial Intelligence is no longer a futuristic idea reserved for academia—it’s becoming an integral part of everyday engineering workflows. This shift is not just about making autocomplete smarter; it's about transforming development into a fluid, intelligent, and collaborative experience. This movement, which we call “Vibe Coding,” is redefining the entire dev process.

Platform Engineering Tech Stack for AI The ever-evolving landscape of DevOps and how advancements in AI are reshaping the way we approach...

AI recruitment is evolving with more specialized roles. While data scientists and developers were once the primary focus, roles like LLM engineers and ML infrastructure architects are now in high demand. AI teams are becoming more cross-functional, requiring candidates to collaborate across disciplines. Recruiters must stay ahead by understanding AI technologies, frameworks, and cloud services, ensuring they can identify the right talent for these rapidly evolving roles.

Illustration depicting the integration of DevOps and AI, highlighting collaboration and continuous improvement in artificial intelligence...

To deploy a Django app in Kubernetes, start by containerizing it with Docker. Create a `Dockerfile` that installs dependencies, sets environment variables, and exposes the port Django runs on. Next, build the Docker image and push it to a container registry. In Kubernetes, define a `Deployment` YAML to specify your Django container, a `Service` to expose it, and a `ConfigMap` for environment variables. Use `kubectl` to apply the configurations, scale the deployment, and monit

Docker Model Runner on Your MacBook for LLM Apps Deepseek, Llama, Python Streamlit

Deploy Flask app with MySQL on Kubernetes

As developers, staying informed about the lifecycle of the tech we rely on is crucial. Spring Framework, one of the most popular Java SDK

"Streamline your development workflow! Learn how to migrate all Azure DevOps items from one organization to another with our expert guide.

Explore the implications of IBM's acquisition of HashiCorp on Terraform users and the future of infrastructure as code. Learn how this deal

The age of AI brings with it a plethora of buzzwords and varying interpretations depending on different people and use cases. In this...