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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.
📅 Month 1: Core Foundations – Linux, Networking & Infrastructure 🎯 Objective: Build rock-solid fundamentals required for production systems 🔧 Topics Covered 🐧 Linux internals (processes, memory, file systems, permissions, systemd) 📜 Advanced shell scripting (Bash, AWK, Sed, Cron jobs) 🌐 Networking fundamentals (TCP/IP, DNS, HTTP/HTTPS, Load Balancing) 🔐 OS-level security basics 🔑 SSH hardening & access control 🧪 Hands-On Labs 🖥️ Hardened Ubuntu Server setup 🌍 Secure NGINX web server deployment 🔁 Reverse proxy & load balancer configuration ☁️ Month 2: Cloud Fundamentals – AWS & Azure from Scratch 🎯 Objective: Understand cloud infrastructure at scale 🛠️ Technologies 🟧 AWS: EC2, VPC, IAM, S3, ALB, Auto Scaling 🟦 Azure: VM, VNets, NSG, Azure Storage 🌐 Cloud networking & identity design 💰 Cost optimization & tagging strategies 🏢 Industry Use Cases 🏗️ AWS infra setup for a TCS-style internal application 🏛️ Multi-tier architecture for an EY consulting workload 🐳 Month 3: Containerization & Kubernetes Engineering 🎯 Objective: Move from VM-based systems to container orchestration ⚙️ Technologies 📦 Docker internals & image optimization ☸️ Kubernetes architecture (API Server, Scheduler, etcd) 📊 Helm charts 🚦 Ingress controllers (NGINX, Traefik) ⚖️ Stateful vs Stateless workloads 🛒 Project 🛍️ Kubernetes-based microservices deployment for an Amazon-like e-commerce backend 🔁 Month 4: CI/CD & DevOps Automation 🎯 Objective: Build automated delivery pipelines 🧰 Technologies 🔧 GitHub Actions, Jenkins, GitLab CI 🏗️ Infrastructure as Code (Terraform) 🛠️ Configuration management (Ansible) 🔄 Blue-Green & Canary deployments 🌍 Real-World Scenarios 🛒 CI/CD pipeline for Walmart-scale application releases 🏢 Automated infra provisioning for a PwC consulting client 🛡️ Month 5: DevSecOps – Security Embedded into Pipelines 🎯 Objective: Shift security left 🔐 Technologies & Practices 🧪 SAST, DAST, SCA 🔑 Secrets management (Vault) 📦 Container security (Trivy, Aqua) ☸️ Kubernetes RBAC & Network Policies 📜 Compliance automation 🧩 Project 📋 DevSecOps pipeline aligned with KPMG audit & compliance standards 📊 Month 6: Observability, Reliability & AIOps Foundations 🎯 Objective: Operate systems intelligently at scale 📈 Technologies 📊 Prometheus & Grafana 📂 ELK Stack (Elasticsearch, Logstash, Kibana) 🧵 Distributed tracing (Jaeger) 🎯 SLA, SLO, Error Budgets 🤖 Introduction to AIOps 🚚 Use Case ⚡ Real-time monitoring for a Blinkit-style logistics platform 🤖 Month 7: MLOps – Machine Learning in Production 🎯 Objective: Operationalize ML systems 🧠 Technologies 🔁 ML pipelines (training, validation, deployment) 📦 Model versioning (MLflow) 🏬 Feature stores ☸️ Kubernetes-based ML serving 🔄 CI/CD for ML models 📈 Project 🛍️ Demand forecasting model deployment for Retail Analytics (Amazon/Walmart inspired) 🧠 Month 8: LLMOps – Managing Large Language Models 🎯 Objective: Deploy and manage LLM-based systems 🛠️ Technologies 🚀 LLM deployment pipelines 🧪 Model fine-tuning workflows 🗂️ Vector databases (Pinecone, FAISS) ✍️ Prompt engineering pipelines 🔌 API gateways for AI services 🏢 Enterprise Scenario 📚 Internal AI assistant for a Deloitte-style consulting knowledge base 🏆 Month 9: Capstone Projects & Enterprise Simulation 🎯 Objective: Deliver production-grade systems end-to-end 🧩 Capstone Options (Choose One) 1️⃣ AI-Powered E-Commerce Platform 🛒 Amazon/Walmart Inspired ☁️ Cloud + ☸️ Kubernetes + 🔁 CI/CD + 🤖 AIOps + 💬 LLM Chatbot 2️⃣ Consulting Firm Cloud Platform 🏢 PwC/KPMG Inspired 🔐 Secure multi-tenant infra + DevSecOps + Compliance dashboards 3️⃣ Real-Time Logistics Intelligence Platform 🚚 Blinkit Inspired 📊 Observability + 📈 Predictive scaling + 🤖 ML-driven alerts 📦 Deliverables 📐 Architecture design documents 💻 GitHub repositories 📊 Monitoring dashboards 🔐 Security & cost reports 🚀 Production-grade deployment 🎓 Outcome & Career Readiness By the end of the program, learners will be able to: ✅ Design & operate enterprise cloud platforms ✅ Build secure, scalable CI/CD pipelines ✅ Manage AI & ML workloads in production ✅ Work as Cloud Engineer, DevOps Engineer, SRE, MLOps Engineer, Platform Engineer 🌟 Why This Program is Different 🔥 Starts from absolute fundamentals 🏁 Ends with real-world, enterprise-grade deployments 🧠 Covers DevOps + AI Operations, not just tools 🏢 Strong alignment with Big 4 consulting & product companies 🎯 Built for placement-backed, outcome-driven learning
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.
