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🎓 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: Cloud & DevOps Architect Multi-cloud architecture CI/CD at scale Security, compliance, FinOps MLOps Engineer Advanced pipelines, ML observability Large-scale model deployment 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
☁️ Cloud Computing with ML Ops – Beginner to Advanced (9 Months) Master the future of tech by combining Cloud Computing, DevOps, and Machine Learning Operations (ML Ops) in one powerful program. This 9-month course takes you from foundational cloud skills to advanced ML deployment, including AWS/GCP, Docker, Kubernetes, Python, MLflow, and more. Learn by building real-world projects and get certified with industry-recognized credentials. Ideal for those aiming to become Cloud ML Engineers, ML Ops Specialists, or DevOps Engineers with AI expertise.
🚀 Launch Your Career in Cloud, DevOps & AI with One Power-Packed Course.
Learn AWS (20+ services), Azure, Git, GitHub, Docker, Kubernetes, Jenkins, Terraform, JIRA, Maven, Ant, and SonarQube. Perfect for IT professionals aiming to build CI/CD pipelines, automate infrastructure, and deploy secure, scalable applications. 100% pre-recorded sessions, hands-on labs, and certification included.
Learn the advance data engineering of Azure setup, user management, and directory services.
