Artificial Intelligence (AI), Machine Learning (ML)

AI/ML & GenAI Professional Advanced

This 8-week weekend intensive is designed to take participants from AI/ML fundamentals through to building, securing, and deploying production-grade Agentic AI systems. Each session follows a structured 2-hour format: the first hour focuses on theory, architecture, and concepts, while the second hour is dedicated to guided hands-on lab work. Topics build on each other deliberately — foundational ML and GenAI knowledge in Weeks 1-2 unlocks the document processing and RAG work in Week 3, which in turn powers the Agentic AI systems built in Week 4. Developer tooling, DevSecOps, and observability in Weeks 5-7 prepare participants for the capstone in Week 8.

Session Format

Hour 1 — Theory & Concepts: Lecture, architecture deep-dive, whiteboard discussion, and Q&A. Participants will understand the 'why' before the 'how.'
Hour 2 — Hands-On Lab: Guided coding exercises, real API integrations, and deployments using the exact tools used in industry. Every lab produces a working artifact participants can keep.

What you’ll learn

Week 1 — AI/ML Foundations & GenAI Landscape
Week 2 — LLMs Deep Dive: Architecture, Training & Use Cases
Week 3 — Document Processing Pipelines: RAG & Vector Databases
Week 4 — Agentic AI: MCP, A2A & Multi-Agent Systems
Week 5 — AI-Powered Coding Tools & Developer Productivity
Week 6 — GenAI DevSecOps: Containers, Kubernetes & Production
Week 7 — GenAI Security, Governance & Observability
Week 8 — Capstone: End-to-End Agentic AI Project

Technology Stack Covered

Models & APIs: GPT-5, Claude 4 (Opus/Sonnet/Haiku), Gemini 3, Llama 3, Amazon Bedrock, HuggingFace
Frameworks: LangChain, LangGraph, AutoGen, LlamaIndex, HuggingFace Transformers, Guardrails AI
Vector Databases: Pinecone, Weaviate, Chroma, pgvector
Developer Tools: VSCode, GitHub Copilot, Cursor, Claude Code, OpenAI Codex, Kiro, Antigravity
Infrastructure: Docker, Kubernetes (minikube + cloud), GitHub Actions, Helm
Observability: OpenTelemetry (OTel), Phoenix by Arize.ai, Server-Sent Events (SSE)

Assessment & Certification

Lab completion and code quality (50%) — weekly labs reviewed against a provided rubric
Participation and peer review (20%) — constructive feedback given to at least two peers per week
Capstone project (30%) — Week 8 end-to-end system evaluated on functionality, security, observability, and deployment quality

Prerequisites

Intermediate Python programming (functions, classes, async/await)
Basic familiarity with REST APIs and JSON
Git and command-line proficiency
Access to OpenAI, Anthropic, and Google AI API keys (free tiers acceptable for Weeks 1-3)
Docker Desktop installed; cloud Kubernetes account recommended for Week 6

Duration: 8 weeks, 32 hours
Fees: On Demand
Language: English
Difficulty: Intermediate
Learning Support: Zoom, individual laptop (student responsibility)
HW Support: Individual laptop (student responsibility) and Wifi at student's home