Learning Roadmap for AWS AI/ML, GenAI & Agentic AI
Learning Roadmap for AWS AI/ML, GenAI & Agentic AI
Kickstart Your Career in AI/ML, GenAI & Agentic AI with This Beginner-Friendly Roadmap!
From zero to AI-ready — Foundations, GenAI, Agentic AI, AWS & Azure AI in 10 stages.
🧠 SECTION 1: CORE AI FOUNDATIONS (Stages 1–4)
📌 Stage 1: Intro to AI, ML, DL & GenAI (Days 1–3)
Objective: Build a strong conceptual foundation in Artificial Intelligence and understand how AI, Machine Learning, Deep Learning and Generative AI relate to each other.
Topics Covered:
What is AI, ML, DL & GenAI — core differences and real-world examples
Evolution of AI — from rule-based systems to LLMs
Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
Discriminative vs Generative AI
Introduction to Large Language Models (LLMs) & Foundation Models
Industry use cases across healthcare, finance, retail and cloud
Career paths — AI Engineer, ML Engineer, GenAI Developer, AI/ML Architect
Goals:
✅ Understand what AI/ML/DL/GenAI are and why they matter in 2026.
✅ Map the AI landscape and identify which role fits your background.
✅ Get ready for hands-on learning in the stages ahead.
📌 Stage 2: ML Foundation (Days 4–8)
Objective: Gain a working understanding of Machine Learning concepts, algorithms and the end-to-end ML workflow.
Topics Covered:
Python for ML — NumPy, Pandas, Matplotlib, Scikit-learn
Data preprocessing, feature engineering & EDA
Supervised Learning — Regression & Classification
Unsupervised Learning — Clustering & Dimensionality Reduction
Model evaluation — accuracy, precision, recall, F1, RMSE
Overfitting, underfitting & hyperparameter tuning
Hands-on: Build your first ML model in Google Colab
📌 Note: Set up your Google Colab account so you can run every notebook alongside the trainer.
Goals:
✅ Write Python code to load, clean and visualise data.
✅ Train and evaluate your first ML models end-to-end.
✅ Understand which algorithm to pick for which problem.
📌 Stage 3: Deep Learning (Days 9–13)
Objective: Move beyond classical ML and build foundational skills in Neural Networks and Deep Learning frameworks.
Topics Covered:
Neural Networks — neurons, layers, activation functions
Forward & backward propagation, gradient descent
TensorFlow & PyTorch essentials
Convolutional Neural Networks (CNNs) for image tasks
Recurrent Neural Networks (RNNs), LSTMs & Transformers
Transfer Learning & pre-trained models
Hands-on: Image classification and text classification labs
Goals:
✅ Understand how deep neural networks learn from data.
✅ Build CNN and Transformer-based models for real datasets.
✅ Get comfortable with TensorFlow/PyTorch workflows.
📌 Stage 4: GenAI Foundation (Days 14–18)
Objective: Understand the world of Generative AI — how LLMs work and how to build with them.
Topics Covered:
Introduction to Generative AI & Foundation Models
How LLMs work — tokens, embeddings, attention, transformers
Prompt Engineering — zero-shot, few-shot, chain-of-thought
Working with OpenAI GPT, Anthropic Claude, Google Gemini & Meta Llama
Fine-tuning vs Prompt Engineering vs RAG — when to use what
Responsible AI — hallucinations, bias, safety and guardrails
Hands-on: Build your first LLM-powered chatbot
Goals:
✅ Explain how modern LLMs generate text, code and images.
✅ Write high-quality prompts that produce reliable outputs.
✅ Build simple GenAI applications using popular LLM APIs.
🚀 SECTION 2: ADVANCED AI • CLOUD AI SERVICES (Stages 5–8)
📌 Stage 5: RAG & Vector Databases (Days 19–22)
Objective: Master Retrieval-Augmented Generation (RAG) — the most in-demand GenAI skill in the job market.
Topics Covered:
Why RAG matters — grounding LLMs with your own data
Embeddings & semantic search
Vector Databases — Pinecone, ChromaDB, Weaviate, FAISS
Chunking strategies & retrieval pipelines
LangChain & LlamaIndex frameworks
Building a Q&A chatbot over PDFs, websites and knowledge bases
Evaluating RAG systems — accuracy, relevance, latency
Goals:
✅ Build production-grade RAG applications end-to-end.
✅ Choose the right vector database for your use case.
✅ Create a domain-specific AI assistant using your own documents.
📌 Stage 6: Agentic AI Foundation (Days 23–27)
Objective: Step into Agentic AI — build autonomous AI agents that reason, plan and take actions.
Topics Covered:
What is Agentic AI — agents vs chatbots vs workflows
Agent architectures — ReAct, Plan-and-Execute, Multi-Agent systems
Tool use & function calling with LLMs
Popular frameworks — LangGraph, CrewAI, AutoGen, LlamaIndex Agents
Memory, planning and reasoning in AI agents
Integrating external tools — Tavily, Serper, APIs, databases
Hands-on: Build a multi-agent research assistant
Goals:
✅ Understand how AI agents think, plan and act.
✅ Build multi-agent systems using LangGraph & CrewAI.
✅ Connect agents to real-world tools and APIs.
📌 Stage 7: AWS AI/ML & GenAI (Days 28–34)
Objective: Gain hands-on expertise with AWS AI/ML services and prepare for AWS AI certifications.
Topics Covered:
AWS AI/ML service landscape overview
Amazon SageMaker — notebooks, training, deployment & MLOps
Amazon Bedrock — foundation models, Agents, Knowledge Bases & Guardrails
AWS AI services — Comprehend, Textract, Polly, Transcribe, Rekognition
Amazon Q — AWS's generative AI assistant
SageMaker JumpStart & Canvas for low-code ML
Certification prep — AIF-C01, MLA-C01, AIP-C01
📌 Note: Set up your AWS Free Tier account before Day 28 to follow along with every lab.
Goals:
✅ Deploy ML models on SageMaker end-to-end.
✅ Build GenAI applications using Amazon Bedrock & Agents.
✅ Prepare confidently for AWS AI Practitioner, ML Associate & AI Engineer exams.
📌 Stage 8: Azure AI/ML & GenAI (Days 35–41)
Objective: Master Microsoft Azure's AI/ML ecosystem and prepare for Azure AI certifications.
Topics Covered:
Azure AI service landscape overview
Azure Machine Learning — workspace, pipelines, endpoints
Azure OpenAI Service — GPT, DALL·E, embeddings
Azure AI Foundry & Prompt Flow
Azure AI Services — Vision, Language, Speech, Document Intelligence
Azure AI Search — RAG on the Microsoft stack
Certification prep — AI-900, AI-102, AI-900 Associate paths
📌 Note: Set up your Azure Free Tier account alongside AWS to compare services side-by-side.
Goals:
✅ Build and deploy ML models using Azure Machine Learning.
✅ Create enterprise-grade GenAI apps with Azure OpenAI.
✅ Prepare confidently for AI-900 and AI-102 certifications.
🧪 SECTION 3: HANDS-ON LABS & MLOps (Stage 9)
📌 Stage 9: Hands-On Labs & MLOps (Days 42–50)
Objective: Bring everything together with real API keys, live cloud accounts and production-grade ML pipelines.
Environment Setup & Tooling:
AWS & Azure Free Tier accounts
OpenAI & Google Gemini API keys
Hugging Face account + Groq API for fast inference
Tavily & Serper API keys for web-search-enabled agents
Google Colab setup for notebooks and GPU access
MLOps Topics Covered:
ML Lifecycle — data, training, deployment, monitoring
Model versioning with MLflow
MLOps pipelines — CI/CD for ML models
Kubeflow for Kubernetes-based ML workflows
Model monitoring, drift detection & retraining
Responsible AI & governance in production
Goals:
✅ Set up every tool and API key needed for a full AI/GenAI workflow.
✅ Build and deploy production-ready ML pipelines.
✅ Operate AI systems in production using MLOps best practices.
🎯 SECTION 4: BUILD PORTFOLIO & LAUNCH CAREER (Stage 10)
📌 Stage 10: Build Portfolio & Launch Career (Weeks 8–12)
Objective: Transition from learner to job-ready AI/ML professional with a strong portfolio and industry certifications.
Portfolio Projects to Build:
End-to-end ML project on SageMaker or Azure ML
RAG-based domain-specific chatbot (PDF/website knowledge base)
Multi-agent AI system using LangGraph or CrewAI
GenAI app using Amazon Bedrock or Azure OpenAI
MLOps pipeline with CI/CD, monitoring and retraining
Certification Path:
1️⃣ AWS Certified AI Practitioner (AIF-C01) — Foundation
2️⃣ Microsoft Azure AI Fundamentals (AI-900) — Foundation
3️⃣ AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Associate
4️⃣ Microsoft Azure AI Engineer Associate (AI-102) — Associate
Career Resources:
Goals:
✅ Build a strong AI portfolio with 4–5 industry-relevant projects.
✅ Earn globally recognised AWS and Azure AI certifications.
✅ Apply confidently for roles like AI Engineer, ML Engineer, GenAI Developer, AI/ML Architect.
🌟 Final Milestone: Land Your First AI/ML Role!
✅ Complete all 10 stages and hands-on labs.
✅ Earn your AIF-C01 and AI-900 certifications.
✅ Build a portfolio with RAG, Agentic AI and MLOps projects.
✅ Showcase your skills on LinkedIn and apply with confidence.
🚀 Your AI career starts here — let's build, learn and grow together!