Department of Computational and Data Sciences (CDS)
Indian Institute of Science (IISc), Bengaluru
Course code: DS 252 · Credits: 3:1 · Semester: Aug 2025
Course page: https://cds.iisc.ac.in/courses/ds252/
This repository contains the laboratory materials, tutorials, and supporting artifacts for DS252 — Introduction to Cloud Computing, offered by the Department of Computational and Data Sciences at the Indian Institute of Science. The course covers virtualization and distributed architectures, cloud service models (IaaS/PaaS/SaaS), cloud storage, serverless and orchestration, DevOps and observability, and emerging topics such as MLOps and the edge–cloud continuum. The labs provide hands-on experience with virtualization, containerization, serverless and hybrid cloud architectures, Kubernetes, observability, and MLOps on modern cloud platforms.
The repository is organized by lab session. Each directory corresponds to a scheduled lab and includes README files, scripts, and configuration as needed.
| Lab Session | Topic |
|---|---|
| lab-session1-2208 | Ubuntu VM setup, Docker, KVM; resource profiling (bare metal, VM, Docker) |
| lab-session2-2908 | Cloud account setup (AWS, Azure), CLI installation, Flask server basics |
| lab-session3-1709 | S3 versioning and lifecycle; ECS (EC2) with ALB; autoscaling; JMeter load testing; FinOps (tagging, budgets) |
| lab-session4-2609 | Serverless workflows: Lambda ingestion, Step Functions (preprocessing, ML inference, aggregation) |
| lab-session5-0310 | Amazon EKS: Kubernetes cluster deployment and scaling |
| lab-session6-1710 | Hybrid architecture with Terraform: Lambda, EC2 (Flask), S3 |
| lab-session7-2410 | IAM (IRSA), OpenTelemetry, Prometheus, and Grafana on EKS |
| lab-session8-3110 | Kubeflow MLOps: Feast, KServe, drift monitoring on AWS EKS |
| lab-session9-1411 | K3s edge computing: lightweight Kubernetes on EC2 |
- An active AWS account (and optionally Azure) with CLI configured; billing may apply beyond free tier.
- Ubuntu 22.04 (bare metal or VM) for labs and consistent environment across labs.
- Basic familiarity with the command line, Python, and Git.
Specific tools (Docker, Terraform, AWS SAM, kubectl, Helm, etc.) are documented in each lab’s README.
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Clone the repository
git clone https://github.com/dream-lab/ds252-2025.git cd ds252-2025 -
Follow lab order
Labs are intended to be done in sequence; later sessions may depend on resources or concepts from earlier ones (e.g., EKS from Lab 5 is reused in Labs 7 and 8). -
Use per-lab READMEs
Eachlab-session*directory contains a README (and sometimes STEPS or TUTORIAL files) with prerequisites, setup, and instructions for that session.
Course content and lab materials are provided for educational use at the Indian Institute of Science (Department of Computational and Data Sciences). Please refer to the course page for more information.