Master DevOps & Cloud
with Real-World Demos
21 hands-on courses on AWS, Azure, GCP, Kubernetes, Terraform & Docker. Learn by building real infrastructure, not watching slides.
What I Teach
Multi-cloud expertise across the technologies that matter most. Every course includes step-by-step demos and companion GitHub repos.
Terraform (Multi-Cloud)
7 courses covering HashiCorp certification, real-world IaC on AWS, Azure & GKE. My primary expertise with the #1 IaC tool.
Kubernetes (EKS/AKS/GKE)
5 courses on managed Kubernetes across all 3 major clouds. Including Helm, AGIC Ingress, and production architectures.
AWS Services
Fargate, CloudFormation, Elastic Beanstalk, CodePipeline, VPC Transit Gateway, and more. Deep AWS expertise.
DevOps & Docker
Real-world DevOps project implementation on AWS. Docker fundamentals to production with 40+ practical demos.
GCP Certification
Google Associate Cloud Engineer certification prep with 150 practical demos. Complete hands-on learning path.
MLOps & AI
Infrastructure to Intelligence. MLOps on AWS, Azure & GCP. AI certification courses coming in 2026.
Why Engineers Choose StackSimplify
Not theory. Not slides. Real infrastructure you build with your own hands.
100% Hands-On Demos
Every course is built around real-world practical demos. You build actual infrastructure, not watch PowerPoint presentations.
GitHub Repos for Every Course
57 public repositories with step-by-step documentation. Fork, follow along, and have working code from day one.
Multi-Cloud Coverage
AWS, Azure, and GCP in a single curriculum. Learn cloud-agnostic patterns and platform-specific implementations side by side.
What Students Say
Real reviews from engineers who learned DevOps, Terraform & Kubernetes with StackSimplify courses.
"This isn't just another Kubernetes tutorial. It's a production-grade, automation-rich, cloud-native implementation that mirrors what top tech companies deploy in real environments."
"Excellent content and well articulated workshops designed to pass not only Terraform certification but also gives practical exposure to Infrastructure as Code. Keep it up. Thank you!"
"There are no words to describe my excitement for taking this course. It seems absolutely amazing!"
"Each and every concept explained clearly and easy manner, with steps in the GitHub repo and slides explaining everything. HIGHLY RECOMMENDED!!!"
"An incredibly well-organized and practical course that mirrors real-world application perfectly. I highly recommend it!"
"A very well-explained course, highly recommended for anyone looking to get into DevOps and understand how things work in real-world production environments."
Weekly DevOps & Cloud insights from a 383K+ Udemy instructor
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Hi, I'm Kalyan Reddy Daida
DevOps & SRE Architect with 18+ years of experience designing complex cloud infrastructure. I've helped 383,000+ engineers worldwide master DevOps through practical, real-world courses.
I believe in learning by doing. Every one of my 21 courses comes with a companion GitHub repository so you can follow along step-by-step. My mission is simple: take the complexity out of cloud infrastructure and make it accessible to everyone.
Latest from the Blog
DevOps insights, tutorials, and cloud tips
5 Levels of ML Model Deployment on Kubernetes
You deploy containers to Kubernetes every day. But how do you deploy ML models? There are 5 levels. Each adds production capabilities. Here’s the progression. The 5 Levels Level Pattern DevOps Equivalent When to Use L1 Baked Image Static binary in container Learning, simple models L2 MLflow Dynamic Config from external store Versioned, no rebuild L3 KServe Predictor Deployment + HPA + Ingress Scalable, zero downtime L4 KServe Transformer Sidecar pattern Modular, independent scaling L5 KServe Explainer Audit logging Compliance, GDPR Level 1: Baked Image Model baked into the Docker image at build time.
5 Questions to Ask Before Every ML Model Deployment
A data scientist hands you a model.pkl and says “deploy this.” What do you ask? Most engineers jump straight to containers and endpoints. But the questions that save you at 2 AM are the ones you ask before deployment, not during an incident. The Checklist # Question Why It Matters 1 What input will break it? Models return garbage confidently on bad input 2 What’s the rollback plan? “Redeploy the old one” is not a plan 3 How do we know it’s broken?
A/B Testing for ML Models: When Offline Metrics Lie
You retrained the model. Accuracy went up 2% on the test set. You deployed it. Revenue dropped 5%. What happened? Offline metrics lie. A model that scores better on historical data can score worse on real users. Canary vs A/B Testing Approach Question It Answers Traffic Split Canary “Does it break anything?” 10-20% to new model A/B Testing “Does it actually improve outcomes?” 50/50 to both models You need both. Canary first, then A/B.
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