GoogleCloud: Vector Search and Embeddings course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

This course offers a practical introduction to vector embeddings and semantic search in modern AI systems, with a focus on real-world applications in natural language processing, recommendation systems, and retrieval-augmented generation (RAG). Through hands-on labs and cloud-based demonstrations, learners will gain foundational skills in deploying vector search solutions on Google Cloud. The course spans approximately 6–8 weeks with a total time commitment of 30–40 hours, including lectures, exercises, and a final project.

Module 1: Foundations of Embeddings

Estimated time: 8 hours

Introduction to vector embeddings and their role in AI
How neural networks generate vector representations
Understanding cosine similarity and other vector metrics
Applications in NLP and multimodal AI systems

Module 2: Vector Search and Semantic Retrieval

Estimated time: 8 hours

Introduction to vector databases and embedding storage
Nearest neighbor search algorithms and efficiency considerations
Semantic search vs. traditional keyword-based search
Retrieval-Augmented Generation (RAG) fundamentals

Module 3: Implementation with Cloud AI Tools

Estimated time: 10 hours

Deploying vector search using Google Cloud managed services
Indexing strategies and performance optimization
Scaling vector search systems in production
Monitoring and evaluating search system performance

Module 4: Embeddings in Natural Language Processing

Estimated time: 6 hours

Text-to-vector conversion techniques
Pre-trained language models and embedding reuse
Use cases in semantic similarity and document retrieval

Module 5: Embeddings in Recommendation Systems

Estimated time: 6 hours

Building item and user embeddings
Personalized recommendations using vector similarity
Integration with AI-powered recommendation engines

Module 6: Final Project