World-Class Curriculum

Modules

AWS
Infrastructure & Services
Data Science
Processing & Analytics
AI
Development & Agents
Augmentation & MCP
Optimization & Protocol Integration

Weekly Breakdown

Week 1
Setup & Copilot Magic
Real Case: Week 1 Course: Intro for Zero Exp—VS Code + Copilot install; no code yet. We help you set up Git, Copilot, AWS account, Python environment, and a developer desktop. You will write IaC code to create and destroy S3, Lambda, and IAM Role with IaC.
Topics:
  • AWS signup, IAM tour via console
  • Free Tier explainer (Copilot: 'Explain AWS costs simply')
  • Developer desktop setup: Git, Copilot, AWS account, Python
  • Hands-on: IaC for S3, Lambda, IAM Role setup
  • Ingest data into S3
  • Read data from S3 bucket using Lambda
  • Data basics: What is CSV? (No code)
Milestone: Copilot setup; run first script
Modules:
AWSData ScienceAIAugmentation & MCP
Week 2
Data Staging Basics
Real Case: Real Case: Kaggle tickets as 'Hyd cab support' (e.g., 'app crash')
Topics:
  • Effortlessly upload data to S3 using real code—no manual steps
  • Automate ticket processing: Lambda reads directly from S3
  • Instantly visualize your data flow—see results as you run code
  • Clean messy CSVs in seconds with Copilot-powered scripts
  • Quickly plot ticket resolution times for actionable insights
  • Instantly filter and analyze 100+ support tickets
  • Embed ticket text for advanced search using sentence-transformers
  • Discover how agent tools supercharge your data retrieval
  • Experience hands-on embedding with real support tickets
  • Connect to AWS FAQ with a single Lambda function
  • Automatically tag and prioritize your support data
  • Summarize ticket batches with AI-powered prompts
Milestone: 100 tickets staged; embedded
Modules:
AWSData ScienceAIAugmentation & MCP
Week 3
RAG Pipeline Starter
Real Case: Real Case: Chunk like 'Ola ride issues' from Hugging Face
Topics:
  • OpenSearch create (console wizard)
  • Copilot: 'Ingest to OpenSearch from S3'
  • Hands-on: Run script; check index
  • Chunking easy: Copilot: 'Split ticket text by sentences'
  • Supercharge your data enrichment: Embed all chunks automatically with Lambda
  • Achieve clean, high-quality data: Remove duplicates and noise using Lambda audit
  • Unlock best-in-class embedding: Compare file-based vs Lambda-powered approaches
  • Empower instant answers: Enable semantic search with a custom Lambda function
  • Accelerate AI adoption: Bedrock models, roles, and policies made simple
  • OpenSearch create (console wizard)
  • Copilot: 'Ingest to OpenSearch from S3'
  • Hands-on: Run script; check index
  • Chunking easy: Copilot: 'Split ticket text by sentences'
  • Hands-on: Check output (no math)
  • RAG flow: Copilot: 'LangChain RAG chain template'
  • LLM call: 'Query with Hugging Face'
  • Hands-on: 'Outage' → chunk retrieval
  • MCP ground: Copilot: 'Add AWS context to prompt'
  • Threshold: 'If low score, alert'
  • Hands-on: Augment one response
Milestone: 300 chunks embedded, deduplicated, and ready for semantic search with Bedrock integration
Modules:
AWSData ScienceAIAugmentation & MCP
Week 4
Retrieval & Simple Gen
Real Case: Real Case: DoorDash 'delay fix' adapted to 'Hyd traffic tickets'
Topics:
  • Unlock instant answers: Lightning-fast Lambda search with k-NN queries
  • Gain full visibility: Add CloudWatch logging for every retrieval
  • Experience real-time results: Test async runs and see immediate feedback
  • Boost relevance: Rerank results by keywords for smarter answers
  • Measure what matters: Check recall and accuracy with easy metrics
  • Spot and fix issues fast: Hands-on troubleshooting with real samples
  • Get grounded, reliable responses: Use Copilot prompts for resolution
  • Achieve next-level accuracy: Agent-powered retrieval and generation
  • See Copilot in action: Output for real support tickets
  • Tap into Bedrock: Fetch prompts for advanced AI workflows
  • Accelerate with speed: Use small models for quick, cost-effective results
  • Reduce hallucinations: Fact-check responses with MCP tools
  • Master rewriters, rerankers, cosine similarity, and Euclidean distance for ultra-accurate, trustworthy answers
Milestone: 3-query test
Modules:
AWSData ScienceAIAugmentation & MCP
Week 5
UI & API Intro
Real Case: Real Case: Flipkart 'order query' chat
Topics:
  • Launch powerful APIs: Instantly set up REST endpoints with API Gateway
  • Secure your app: Add authentication in minutes with Cognito
  • Build modern UIs: Create interactive apps with Streamlit
  • Capture feedback effortlessly: Log user actions to CSV with Copilot
  • Visualize insights: Generate bar charts for instant query analysis
  • Compare and optimize: Test multiple UI versions for best results
  • Empower users: Add smart RAG input fields with Streamlit agents
  • Keep conversations contextual: Remember chat history for seamless support
  • Demo real-world help: Provide instant EC2 assistance via UI
  • Boost support: Add AWS help buttons for one-click guidance
  • Measure quality easily: Check scores with simple built-in tools
  • Integrate AI responses: Link MCP outputs directly in your UI
Milestone: Local UI and API live—query Lambda, evaluate RAG, and deliver instant support
Modules:
AWSData ScienceAIAugmentation & MCP
Week 6
Deploy & Interact
Real Case: Real Case: Zomato 'food delay' bot deploy
Topics:
  • Deploy with confidence: Launch your solution using Lambda—pay only when you use it
  • Optimize user experience: Set up throttling for smooth scaling
  • Store and rate instantly: Use DynamoDB for real-time feedback
  • Visualize success: Evaluate results using interactive Streamlit app dashboards
  • Analyze conversations: Dive into 10 real chat sessions for actionable learnings
  • Deliver seamless support: Enable multi-turn follow-ups with smart agents
  • Integrate full RAG: Power your Streamlit app with advanced retrieval and generation
  • Simulate real tickets: Hands-on practice for real-world scenarios
  • Expose live insights: Use MCP API for instant analytics
  • Measure speed: Track and optimize response times for peak performance
  • Get smart UI suggestions: Enhance your interface with MCP-driven recommendations
  • Set up FAQ routers, personalize user journeys, reduce latency, and validate performance for a flawless rollout
Milestone: Production-ready deployment with personalized support, low latency, and validated performance—ready for real users
Modules:
AWSData ScienceAIAugmentation & MCP
Week 7
MCP Server & Client Intro
Real Case: Real Case: Set up a sample MCP server and client for cybersecurity alert tickets (Hyd/Anduril use case)
Topics:
  • Get started: MCP Server and Client architecture overview
  • Hands-on: Spin up a sample MCP server for ticket processing
  • Connect your client: MCP Client setup and integration
  • Cache for speed: Add Redis to MCP workflows
  • Scale easily: Lambda auto-config for MCP endpoints
  • Monitor costs: Use Cost Explorer for MCP deployments
  • Fine-tune for your data: LoRA intro on small ticket sets
  • Spot errors fast: Hallucination check scripts for MCP outputs
  • Personalize responses: Add custom logic to MCP Client
  • Secure your pipeline: Best practices for MCP server/client security
  • Analyze and optimize: Review performance and accuracy
  • Deploy and test: End-to-end MCP workflow simulation
  • Hands-on: Latency graph
  • Hybrid search: Copilot: 'Keywords + vectors'
  • MCP action: 'Multi-tool agent'
  • Hands-on: Route secure queries
  • Custom MCP: 'Build one tool prompt'
  • Scores: 'Add confidence'
  • Hands-on: Bias flag in outputs
Milestone: Sample MCP server and client deployed, integrated, and optimized for real-world ticket processing
Modules:
MCP ServerMCP Client
Week 8
Capstone & Polish
Real Case: Real Case: Build a mentor query bot for T-Hub startup using agentic AI and MCP RAG agent
Topics:
  • Save costs: Automate cleanup and teardown for efficient resource use
  • Benchmark your solution: Compare baselines with Copilot-powered evals
  • Showcase results: Visualize your portfolio with interactive graphs
  • Scale up: Run 200+ samples for robust testing
  • Empower teams: Build multi-user agentic AI systems with Copilot
  • Build ethical AI: Address bias in local data for fair outcomes
  • Polish your project: Apply final tweaks for a professional finish
  • Add advanced search: Integrate MCP web tools for powerful queries
  • Customize for impact: Augment with Hyd-specific features (e.g., weather)
Milestone: Team-built agentic AI system using MCP and RAG agent, with polished UI, metrics, and T-Hub pitch-ready demo
Modules:
MCPAI RAG AgentTeamwork