Teaching
Online courses and educational content on AI, machine learning, and vector databases.
I build courses that bridge the gap between AI research and real-world implementation. From production RAG systems to multimodal search, my courses are hands-on, code-first, and designed for practitioners.
Courses
Retrieval Augmented Generation (RAG)
Build production-ready RAG systems from the ground up. Covers retrievers, vector databases, LLMs, prompt engineering, agentic RAG, evaluation, and real-world deployment with hands-on labs using Weaviate and Together AI.
What You'll Build:
- Hybrid search pipeline with BM25 and semantic search
- Domain-specific chatbot with dynamic pricing
- Monitored, production-grade RAG system
Vector Databases: from Embeddings to Applications
A concise, hands-on introduction to vector databases. Learn how embeddings capture meaning, explore ANN algorithms for fast search, and build applications from hybrid search to multilingual retrieval.
What You'll Build:
- Vector embedding and similarity search pipelines
- Sparse, dense, and hybrid search systems
- Multilingual search application
Building Multimodal Search and RAG
Go beyond text-only RAG. Learn contrastive learning, build any-to-any multimodal search across text, images, and video, and implement end-to-end multimodal RAG with visual instruction tuning.
What You'll Build:
- Contrastive learning on a real dataset
- End-to-end multimodal RAG system
- Multi-vector recommender system
Introduction to AI-Native Vector Databases
Understand why vector databases are a critical infrastructure layer for modern AI. Covers fundamentals, AI-native architectures, real-world use cases, and best practices for professionals entering the space.
What You'll Learn:
- Vector database fundamentals and architectures
- Real-world AI infrastructure use cases
- Industry best practices and patterns