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
ML Security on Kubernetes: 4 Layers Protecting Your Models
Your model endpoint has no auth. Anyone with the URL gets predictions. That is not a hypothetical. It is the default on most KServe deployments. Deploy a model, get an endpoint, and it is wide open. No token. No identity check. No network restriction. ML systems have a unique attack surface: training data, model artifacts, feature stores, and inference endpoints. Each one is a target. The ML Attack Surface Asset Default Risk Model endpoints Open, returning predictions to anyone Training data S3 buckets with broad IAM access Model artifacts Serialized files that can be swapped or poisoned Feature stores Real-time pipelines with PII and business logic Traditional DevOps secures code.
GPU Scheduling on Kubernetes: MIG, Time-Slicing, and Node Pools
One NVIDIA A100 GPU costs $3 per hour on AWS. Your inference pod uses 12% of it. The other 88% sits idle, billed, and wasted. Kubernetes schedules GPUs as whole devices by default. One pod gets one GPU. No sharing. No slicing. Massive waste for inference workloads. The Problem: One GPU, One Pod A fraud detection model needs 2GB of GPU memory and runs a few requests per second. The node has an A100 with 40GB.
Batch vs Real-Time ML Inference: 90% of Predictions Can Be Batch
Your model runs in real-time. 90% of your predictions do not need to. That is the most expensive assumption in ML infrastructure. A recommendation engine that refreshes daily does not need always-on pods. A credit risk score computed once at application time does not need a replica running at 3 AM. Most teams default to real-time because that is how their first model shipped. Every model after inherits the same pattern.
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