Description

GenAIOps: Build Like You Already Work There — on AWS Cloud

Zero to Hero Program | LWPLABS – IT Polytechnic School

Duration: 40+ Hours (2 Months) · Format: Live + Recorded, Lifetime Access · Level: Beginner to Advanced

Overview

GenAIOps is not structured like a traditional course — it's structured like a workplace. From Day 1, you'll work the way real AI engineering teams do: cloning repos instead of copy-pasting code, opening pull requests instead of pushing straight to main, managing IAM roles and cost cleanup instead of ignoring infrastructure, and shipping to real AWS services instead of toy sandboxes.

Most GenAI courses try to stay cloud-agnostic and end up shallow everywhere. GenAIOps goes deep on AWS specifically — because that's the cloud most companies actually standardize their workloads on. Everything you build here — IAM roles, Docker containers, Kubernetes deployments, Bedrock agents, SageMaker endpoints — is what you'd actually touch on day one of an AI engineering job, not just in an isolated demo.

By the end, you won't just understand GenAI concepts — you'll have 10 real, deployed projects to show for it.

Why This Program Is Different

• A workplace, not a lecture hall. Feature branches, pull requests, code review, and peer approval are part of the workflow from week one.
• AWS-native, not cloud-agnostic. Deep, hands-on work with EC2, S3, IAM, Bedrock, SageMaker, and EKS — the stack most employers already run on.
• Real projects, not toy demos. Every project is deployed to live infrastructure, not just run locally and forgotten.
• Mentorship, not just instruction. Live sessions with an AI & Cloud expert, career guidance, and interview preparation built into the curriculum.

What You'll Learn

Phase 1 — Foundations Lab setup, Git & GitHub workflows, Python fundamentals (data types, control flow, functions, OOP), NumPy & Pandas for data manipulation, logging best practices.

Phase 2 — Backend & Data Engineering Building REST APIs with Flask, Data Version Control (DVC) for large datasets, experiment tracking with MLflow, deterministic guardrails for filtering unsafe inputs.

Phase 3 — Cloud Infrastructure & MLOps Docker containerization, Kubernetes (Kind, KServe, EKS concepts), Amazon SageMaker domains and endpoints, AWS networking (VPC, subnets, security groups), CI/CD pipelines with GitHub Actions and Argo CD (GitOps).

Phase 4 — GenAI Engineering LangChain & LangGraph, building ReAct agents with memory and Human-in-the-Loop approval, Retrieval-Augmented Generation (RAG), PII redaction middleware for compliance (PCI DSS, HIPAA), Amazon Bedrock & Bedrock AgentCore, Model Context Protocol (MCP).

Phase 5 — Production & Career Readiness System design for production-grade AI systems (circuit breakers, rate limiting, observability), running models locally, AI interview preparation, and a full capstone project.

The 10 Projects

1. AI Fundamentals — Work hands-on with system prompts, embeddings, vectors, and core AI concepts
2. Deterministic Guardrails — Build an input filter to block harmful or off-topic queries
3. Flask API on AWS — Build and deploy a live web application end-to-end on EC2
4. MLflow Experiment Tracking — Track and compare real ML experiments (Wine Quality case study)
5. Docker + Kubernetes Deployment — Containerize an app and deploy it on a real Kubernetes cluster
6. ML Model Serving with KServe — Deploy a customer churn prediction model on Kubernetes
7. CI/CD for ML — Build a pipeline that automatically retrains and redeploys your model on every push
8. Autonomous ReAct Agent — Build an agent with memory and human-in-the-loop approval for sensitive actions
9. Enterprise RAG Chatbot — Build a RAG system with PII redaction for banking/healthcare use cases
10. Capstone: Agentic HR System — A full end-to-end agentic workflow: resume parsing, candidate scoring, and automated notifications

Who This Is For

Whether you're starting from zero or already writing code, this program is built to take you from foundational Python to deploying production-grade AI agents on AWS. No prior cloud experience required — just a willingness to build, break, and fix things like you would on a real team.

What You Get

• 40+ hours of live, hands-on mentorship
• 10+ real-time, deployed projects
• Certificate of completion
• Lifetime access to all recorded sessions
• Career guidance & interview preparation

Instructor: Kapil Sir — AI & Cloud Expert