Courses Descriptions

🎓 12-Month Master Program: Cloud, DevOps, DSA, MLOps & GenAI

📍 Phase 1: Foundations (Month 1 – Month 3)

Month 1 – Cloud Basics & DSA Foundations

• Cloud: Intro to Cloud Computing, IaaS/PaaS/SaaS, AWS/Azure/GCP overview

• DSA: Complexity Analysis, Arrays, Strings, Recursion

• Hands-on Project: Deploy a static website on AWS S3 + Basic DSA coding challenges

Month 2 – DevOps Fundamentals

• Version Control: Git, GitHub/GitLab workflows

• CI/CD Basics: Jenkins, GitHub Actions

• DSA: Searching & Sorting, Linked Lists

• Hands-on Project: Set up a CI/CD pipeline for a sample app

Month 3 – Cloud Core Services + DSA Expansion

• Cloud: Compute (EC2, VM), Storage (S3, Blob), Networking (VPC)

• DSA: Stacks, Queues, Hashing

• Hands-on Project: Build a 3-tier cloud architecture + DSA problem sets

📍 Phase 2: Intermediate (Month 4 – Month 6)

Month 4 – DevOps Intermediate + Cloud IAM

• Cloud: IAM, Security, Monitoring (CloudWatch, Azure Monitor)

• DevOps: Docker basics, Containerization

• DSA: Trees (Binary Trees, BST)

• Hands-on Project: Dockerize a web app + IAM role-based access project

Month 5 – Kubernetes & IaC

• DevOps: Kubernetes basics (Pods, Deployments, Services)

• IaC: Terraform, Ansible

• DSA: Graphs (BFS, DFS, Shortest Path)

• Hands-on Project: Deploy microservices on Kubernetes

Month 6 – Cloud Native & Advanced DevOps

• Cloud: Serverless (AWS Lambda, Azure Functions, GCP Functions)

• DevOps: Advanced CI/CD, GitOps (ArgoCD)

• DSA: Dynamic Programming basics

• Hands-on Project: End-to-end Serverless app with CI/CD pipeline

📍 Phase 3: Advanced (Month 7 – Month 9)

Month 7 – MLOps Foundations

• MLOps: ML lifecycle, Data pipelines, DVC, MLflow

• Cloud: Managed AI/ML services (AWS Sagemaker, Azure ML)

• DSA: Advanced DP, Greedy algorithms

• Hands-on Project: Train & track ML experiments with MLflow

Month 8 – MLOps Deployment

• Deployment: FastAPI/Flask model serving

• CI/CD for ML: Kubeflow pipelines

• Monitoring: Drift detection, logging

• Hands-on Project: Deploy ML model on Kubernetes with monitoring

Month 9 – Generative AI Foundations

• GenAI: Transformer basics, LLMs overview (GPT, LLaMA, BERT)

• Prompt Engineering

• Tools: Hugging Face, LangChain basics

• Hands-on Project: Build a simple GenAI chatbot with OpenAI API

📍 Phase 4: Specialization (Month 10 – Month 12)

Month 10 – GenAI Applications & DSA Advanced

• GenAI: RAG (Retrieval Augmented Generation), Fine-tuning (LoRA, PEFT)

• Applications: Chatbots, Image generation, Speech AI

• DSA: Backtracking, Segment Trees, Bit Manipulation

• Hands-on Project: Custom knowledge chatbot with LangChain + Vector DB

Month 11 – Specialization Track Selection

Students choose one specialization:

1. Cloud & DevOps Architect

   • Multi-cloud architecture

   • CI/CD at scale

   • Security, compliance, FinOps

2. MLOps Engineer

   • Advanced pipelines, ML observability

   • Large-scale model deployment

3. GenAI Engineer

   • Fine-tuning LLMs

   • Building multimodal apps (text + image + speech)

Hands-on Project: Capstone preparation aligned with specialization

Month 12 – Capstone & Career Prep

• Capstone Projects:

  • Cloud/DevOps → Multi-Cloud E-commerce infra with CI/CD

  • MLOps → End-to-end ML pipeline with monitoring

  • GenAI → AI Copilot app (Chatbot + RAG + API integration)

• Career Prep: Resume, Interview training, Mock interviews

• Final Demo Day: Present capstone projects

🎯 Outcome & Certification

By end of the program, learners graduate as:

• Cloud & DevOps Architect (if specialization chosen)

• MLOps Engineer (if specialization chosen)

• GenAI Engineer (if specialization chosen)

• Plus strong foundation in DSA for coding interviews