This module introduces the core components of Azure data engineering, focusing on account creation, user management, and organizational setup. Students will learn how to configure Azure environments to support business needs and access control through user roles, groups, and domain management.
Classes:
Class 1: Azure Account Creation β Create and configure Azure accounts.
Class 2: Azure User Creation & Deletion β Manage Azure Active Directory users.
Class 3: Azure Static and Dynamic Groups β Understand and configure group types.
Class 4: Azure Custom Domain Naming β Add and configure custom domains.
Class 5: Azure Company Branding β Customize Azure AD branding for organizations.
Short Description: Secure Azure environments using MFA, policies, and identity management.
Detailed Description:
Dive deep into Azure identity protection, multifactor authentication, and conditional access policies. Learn how to apply security best practices to safeguard enterprise data and ensure compliant user access.
Classes:
Class 6: Azure Multifactor Authentication - Part 1 β Set up MFA using Azure.
Class 7: Azure Multifactor Authentication - Part 2 β Advanced MFA settings and policies.
Class 8: Conditional Access Policy β Define and apply policies based on user/device conditions.
Class 9: Directory Creation & Deletion β Create and manage Azure directories securely.
Short Description: Learn how to deploy, manage, and organize Azure resources effectively.
Detailed Description:
Gain practical experience managing Azure resources, creating virtual machines, provisioning SQL databases, and exploring the infrastructure differences between physical and virtual Azure environments.
Classes:
Class 10: Creation of Resource Group β Organize resources efficiently.
Class 11: Creation of VMs & Adding Disks β Deploy virtual machines and attach storage.
Class 12: Azure SQL β Set up and manage Azure SQL databases.
Class 13: Physical & Virtual Azure VMs β Compare and configure VM types.
Class 14: Resource Group & Virtual Machine Deep Dive β Combine multiple resources for integrated solutions.
Short Description: Understand the data engineering ecosystem in Azure.
Detailed Description:
Get introduced to Azure Data Engineering, including the tools and platforms used to ingest, process, and manage large-scale data. This demo session sets the foundation for upcoming real-world data integration techniques.
Classes:
Session: Azure Data Engineering Demo β Overview of data pipeline architecture and Azure tools.
Short Description: Build foundational programming and database querying skills.
Detailed Description:
This module introduces Python and SQLβtwo core skills for any data engineer. Learn data structures, logic, querying, and integration techniques that you'll use while building ETL pipelines and performing data analysis.
Classes:
Part 1: Introduction to Python and SQL β Basics of syntax, types, and commands.
Part 2: Python Control Structures & SQL Functions β Use logic to drive programs.
Part 3: Working with Data β Learn lists, dictionaries, and SQL queries.
Part 4: SQL Joins and Subqueries β Combine multiple tables and write advanced queries.
Part 5: Real-world Problems and Integration β Practice data processing and automation with real datasets.
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π 12-Month Master Program: Cloud, DevOps, DSA, MLOps & GenAI π Phase 1: Foundations (Month 1 β Month 3) Month 1 β 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 β 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 β 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 π Phase 2: Intermediate (Month 4 β Month 6) Month 4 β 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 β 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 β 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 π Phase 3: Advanced (Month 7 β Month 9) Month 7 β 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 β 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 β 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 π Phase 4: Specialization (Month 10 β Month 12) Month 10 β 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 β 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 β Capstone & Career Prep Capstone Projects: Cloud/DevOps β Multi-Cloud E-commerce infra with CI/CD MLOps β End-to-end ML pipeline with monitoring GenAI β AI Copilot app (Chatbot + RAG + API integration) Career Prep: Resume, Interview training, Mock interviews Final Demo Day: Present capstone projects π― 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
βοΈ Cloud Computing with ML Ops β 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.
π Month 1: Core Foundations β Linux, Networking & Infrastructure π― Objective: Build rock-solid fundamentals required for production systems π§ Topics Covered π§ Linux internals (processes, memory, file systems, permissions, systemd) π Advanced shell scripting (Bash, AWK, Sed, Cron jobs) π Networking fundamentals (TCP/IP, DNS, HTTP/HTTPS, Load Balancing) π OS-level security basics π SSH hardening & access control π§ͺ Hands-On Labs π₯οΈ Hardened Ubuntu Server setup π Secure NGINX web server deployment π Reverse proxy & load balancer configuration βοΈ Month 2: Cloud Fundamentals β AWS & Azure from Scratch π― Objective: Understand cloud infrastructure at scale π οΈ Technologies π§ AWS: EC2, VPC, IAM, S3, ALB, Auto Scaling π¦ Azure: VM, VNets, NSG, Azure Storage π Cloud networking & identity design π° Cost optimization & tagging strategies π’ Industry Use Cases ποΈ AWS infra setup for a TCS-style internal application ποΈ Multi-tier architecture for an EY consulting workload π³ Month 3: Containerization & Kubernetes Engineering π― Objective: Move from VM-based systems to container orchestration βοΈ Technologies π¦ Docker internals & image optimization βΈοΈ Kubernetes architecture (API Server, Scheduler, etcd) π Helm charts π¦ Ingress controllers (NGINX, Traefik) βοΈ Stateful vs Stateless workloads π Project ποΈ Kubernetes-based microservices deployment for an Amazon-like e-commerce backend π Month 4: CI/CD & DevOps Automation π― Objective: Build automated delivery pipelines π§° Technologies π§ GitHub Actions, Jenkins, GitLab CI ποΈ Infrastructure as Code (Terraform) π οΈ Configuration management (Ansible) π Blue-Green & Canary deployments π Real-World Scenarios π CI/CD pipeline for Walmart-scale application releases π’ Automated infra provisioning for a PwC consulting client π‘οΈ Month 5: DevSecOps β Security Embedded into Pipelines π― Objective: Shift security left π Technologies & Practices π§ͺ SAST, DAST, SCA π Secrets management (Vault) π¦ Container security (Trivy, Aqua) βΈοΈ Kubernetes RBAC & Network Policies π Compliance automation π§© Project π DevSecOps pipeline aligned with KPMG audit & compliance standards π Month 6: Observability, Reliability & AIOps Foundations π― Objective: Operate systems intelligently at scale π Technologies π Prometheus & Grafana π ELK Stack (Elasticsearch, Logstash, Kibana) π§΅ Distributed tracing (Jaeger) π― SLA, SLO, Error Budgets π€ Introduction to AIOps π Use Case β‘ Real-time monitoring for a Blinkit-style logistics platform π€ Month 7: MLOps β Machine Learning in Production π― Objective: Operationalize ML systems π§ Technologies π ML pipelines (training, validation, deployment) π¦ Model versioning (MLflow) π¬ Feature stores βΈοΈ Kubernetes-based ML serving π CI/CD for ML models π Project ποΈ Demand forecasting model deployment for Retail Analytics (Amazon/Walmart inspired) π§ Month 8: LLMOps β Managing Large Language Models π― Objective: Deploy and manage LLM-based systems π οΈ Technologies π LLM deployment pipelines π§ͺ Model fine-tuning workflows ποΈ Vector databases (Pinecone, FAISS) βοΈ Prompt engineering pipelines π API gateways for AI services π’ Enterprise Scenario π Internal AI assistant for a Deloitte-style consulting knowledge base π Month 9: Capstone Projects & Enterprise Simulation π― Objective: Deliver production-grade systems end-to-end π§© Capstone Options (Choose One) 1οΈβ£ AI-Powered E-Commerce Platform π Amazon/Walmart Inspired βοΈ Cloud + βΈοΈ Kubernetes + π CI/CD + π€ AIOps + π¬ LLM Chatbot 2οΈβ£ Consulting Firm Cloud Platform π’ PwC/KPMG Inspired π Secure multi-tenant infra + DevSecOps + Compliance dashboards 3οΈβ£ Real-Time Logistics Intelligence Platform π Blinkit Inspired π Observability + π Predictive scaling + π€ ML-driven alerts π¦ Deliverables π Architecture design documents π» GitHub repositories π Monitoring dashboards π Security & cost reports π Production-grade deployment π Outcome & Career Readiness By the end of the program, learners will be able to: β Design & operate enterprise cloud platforms β Build secure, scalable CI/CD pipelines β Manage AI & ML workloads in production β Work as Cloud Engineer, DevOps Engineer, SRE, MLOps Engineer, Platform Engineer π Why This Program is Different π₯ Starts from absolute fundamentals π Ends with real-world, enterprise-grade deployments π§ Covers DevOps + AI Operations, not just tools π’ Strong alignment with Big 4 consulting & product companies π― Built for placement-backed, outcome-driven learning