Workshops & seminars

Short, intensive programs to level up your skills and stay current with the rapidly evolving AI landscape.

Workshops

GenAILangChainRAG
1 day · 8 hrs·In-person·Intermediate

Build a RAG Chatbot in a Day

Build a production-ready Retrieval-Augmented Generation chatbot using LangChain, ChromaDB, and OpenAI — from document ingestion to deployed app.

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MLDockerFastAPI
1 day · 8 hrs·In-person·Beginner friendly

ML Model to API — End-to-End Deployment

Train an ML model, containerize with Docker, build a FastAPI wrapper, and deploy to the cloud — all in one intensive session.

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Agentic AILangGraph
2 days · 16 hrs·Hybrid·Advanced

Multi-Agent Systems with LangGraph

Design and build a multi-agent AI system that can research, write, and review content autonomously using LangGraph.

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GenAIPrompting
Half day · 4 hrs·Online·All levels

Prompt Engineering Masterclass

Master advanced prompting techniques — chain-of-thought, few-shot, structured outputs, and systematic prompt optimization.

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GenAIFine-Tuning
1 day · 8 hrs·In-person·Advanced

Fine-Tuning LLMs on Custom Data

Hands-on workshop on fine-tuning open-source LLMs using LoRA/QLoRA with your own datasets. Covers data prep, training, and evaluation.

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Event calendar

April 2026

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Workshop
Seminar

Upcoming in April

Apr12

Build a RAG Chatbot

10:00 AM – 6:00 PM

workshop
Apr13

Prompt Engineering Masterclass

2:00 PM – 6:00 PM

workshop
Apr19

AI Safety & Ethics Seminar

5:00 PM – 7:00 PM

seminar
Apr20

ML Model to API Workshop

10:00 AM – 6:00 PM

workshop
Apr26

Career Paths in AI/ML

4:00 PM – 6:00 PM

seminar
Apr27

Multi-Agent Systems

10:00 AM – 6:00 PM

workshop
Apr28

Multi-Agent Systems (Day 2)

10:00 AM – 6:00 PM

workshop

Seminars & talks

Panel DiscussionIndustry Expert Panel

The Future of Agentic AI in Enterprise

A deep dive into how autonomous AI agents are reshaping workflows across finance, healthcare, and software engineering.

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SeminarGuest Speaker Series

AI Safety & Responsible Development

Understanding alignment, bias, and safety considerations when building AI systems that interact with the real world.

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TalkIndustry Practitioner

From Research to Production: Scaling AI

Lessons from deploying ML/AI systems at scale — infrastructure, monitoring, and the gap between notebooks and production.

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