Master vector databases and transform how AI systems find, retrieve, and generate information. This program teaches you to build semantic search and retrieval-augmented generation (RAG) systems that understand context beyond traditional keyword matching.
You will convert raw data into vector representations, master Chroma and Weaviate, and implement search techniques used by leading tech firms. The curriculum bridges academic concepts with real-world challenges in tech, finance, and healthcare.
This will prepare you for entry-level and mid-career roles, such as, ML Engineer, AI Infrastructure Specialist, and Data Scientist. These skills are critical for professionals specializing in cutting-edge AI infrastructure.
Key learning objectives:
Implement embedding pipelines for text and multimodal data.
Design scalable vector database architectures.
Build production-ready semantic search and RAG systems.
Secure and monitor vector search infrastructure.
Prerequisites: Working knowledge of Python and basic machine learning concepts is recommended. Ideal for those comfortable with command-line tools.
Unique program features:
Comprehensive Chroma and Weaviate vector database coverage
Hands-on projects simulating real-world engineering scenarios
GenAI literacy modules
Career development support
Upon completion, you'll have portfolio-ready projects, professional certification, and deployable skills to build the next generation of intelligent systems.
Applied Learning Project
This program features three portfolio projects that apply course concepts to real problems:
End-to-end Embedding Pipeline: Build an embedding extraction pipeline, populate a vector store, and evaluate similarity search performance under realistic conditions.
Chroma-Powered Knowledge Base: Create a semantic search application using Chroma, optimize collections, and measure retrieval relevance and latency.
Production RAG Pipeline: Integrate a vector store with an LLM to build a RAG system with logging, caching, monitoring, and deployment patterns; demonstrate migration and reliability checks.
Add-on GenAI modules teach AI-assisted embeddings and similarity analysis and query optimization. Each project emphasizes reproducible notebooks, performance metrics, and clear documentation suitable for a professional portfolio.
