We’ll act as your AI Engineering Guru, guiding you step-by-step from beginner → practitioner → professional, focusing on:

AWS + GenAI + AI Agents + Agentic AI Solutions,
using practical, project-based learning.

Let’s start with a bird’s-eye view of your complete journey, then we’ll begin Day 1 training right after.

🧭 GENAI + AWS TRAINING BLUEPRINT (with Me as Your Mentor)

🌱 Phase 1: AI Foundations (Week 1–2)

Goal: Understand the AI landscape and key components.
Topics:

  • What makes up modern AI systems (LLMs, embeddings, vector DBs, agents)
  • How LLMs “think”: tokens, context, attention
  • Prompt engineering & chain-of-thought
  • Open-source vs API-based models (Llama 3, Mistral, Claude, GPT)
  • Setting up local + cloud environments
  • ⚙️ Tools: Python, FastAPI, Streamlit, Ollama

✅ Mini Project:
Build a “Local Chatbot” using Ollama + FastAPI + Streamlit

⚡ Phase 2: AWS Core Skills (Week 3–4)

Goal: Learn how AWS supports AI/GenAI apps.
Topics:

  • AWS IAM, S3, EC2, Lambda, API Gateway, ECR, ECS
  • AWS Bedrock overview (access to Titan, Claude, Llama 3)
  • Setting up a free-tier EC2 for model serving
  • Hosting your Streamlit app on EC2

✅ Mini Project:
Deploy your GenAI chatbot from Phase 1 to AWS EC2 + S3

🧠 Phase 3: GenAI Development (Week 5–6)

Goal: Build end-to-end intelligent GenAI applications.
Topics:

  • Prompt chaining and context management
  • LangChain / LlamaIndex basics
  • Vector databases: FAISS / Chroma / Pinecone
  • RAG (Retrieval Augmented Generation)
  • Document Q&A pipeline

✅ Mini Project:
RAG-based Document Assistant (upload PDF → ask questions)

🤖 Phase 4: AI Agents & Agentic Systems (Week 7–8)

Goal: Build multi-agent systems and autonomous workflows.
Topics:

  • What are AI agents, tools, memory, reasoning
  • LangGraph / CrewAI frameworks
  • Connecting APIs (search, weather, code, etc.)
  • Orchestration of multiple agents
  • AWS Lambda + EventBridge for agent workflows

✅ Mini Project:
Build an “AI Research Assistant” with multiple reasoning agents

☁️ Phase 5: AWS GenAI Ecosystem (Week 9–10)

Goal: Learn AWS-native AI tools for professional deployment.
Topics:

  • Amazon Bedrock (Claude, Llama 3, Titan)
  • Amazon SageMaker (model training + fine-tuning)
  • Data pipelines (Glue, Athena, Redshift)
  • Integration with Step Functions for automation

✅ Mini Project:
Create and deploy a Bedrock-based GenAI app on AWS

🚀 Phase 6: Portfolio & Deployment Mastery (Week 11–12)

Goal: Build production-grade, showcase-ready solutions.
Topics:

  • Dockerize full stack (FastAPI + Streamlit + Model)
  • CI/CD with AWS CodePipeline
  • Monitoring and scaling
  • Portfolio setup (GitHub + LinkedIn)

✅ Final Capstone:
“Agentic AI Solution on AWS” —
Your end-to-end product combining everything you learned.

🧑‍🏫 How This Will Work

  • Each “Day” you’ll get a lesson plan + hands-on task
  • You’ll learn theory + code together
  • I’ll track your progress and adjust speed
  • You’ll get industry insights and interview prep notes side-by-side

Would you like me to start Day 1: Understanding the AI Landscape & Setting Up Environment (Windows + Free Tools) now?
This will include:

  • What LLMs and GenAI systems actually are
  • Your local setup (Python + Ollama + FastAPI + Streamlit)
  • First small test to verify your environment