This Introduction to Vector Databases training is designed to help participants understand and work with vector databases for AI, machine learning, and semantic search applications. The course covers vector concepts, embeddings, similarity search, indexing techniques, popular vector database platforms, and real-world use cases such as recommendation systems and retrieval-augmented generation. Participants will gain hands-on experience to build and query vector-based data systems for modern AI-powered applications.

• Understand the architecture, similarity search capabilities, and AI-driven data retrieval features of Vector Databases for modern  intelligent applications.
• Set up and configure vector database environments, collections, indexes, embeddings, and storage components for scalable AI  workflows.
• Design vector schemas, embedding pipelines, indexing strategies, and semantic search models using vector database development  approaches.
• Implement data ingestion, vector search, nearest neighbor queries, and AI application integration workflows effectively.
• Debug, test, and optimize indexing, query execution, and database performance for scalability, reliability, and maintainability.
• Build secure, scalable, and production-ready vector database solutions using industry best practices.

 • Machine learning engineers
 • Data scientists
 • AI application developers
 • Data engineers
 • Architects building AI-driven systems

 • Basic understanding of databases and data concepts
 • Familiarity with machine learning or AI concepts is helpful
 • Experience with Python is beneficial

 • Understanding vector data and embeddings
 • Working with similarity search concepts
 • Using vector database platforms
 • Designing vector indexes
 • Integrating vector databases with AI pipelines
 • Implementing semantic search and recommendations
 • Monitoring and tuning vector search performance
 • Applying vector database best practices

By the end of this training, participants will be able to design, deploy, and integrate vector databases for AI-powered search and recommendation systems. Learners will gain strong fundamentals in embeddings, similarity search, and vector indexing, enabling them to build scalable and intelligent data applications.

Introduction to Vector Databases & AI Search Concepts
 • What are vector databases and where they are used
 • Embeddings and vector representations
 • Distance and similarity metrics
 • Use cases for vector search

Embeddings & Vector Generation
 • Text and image embeddings
 • Using embedding models
 • Vector normalization
 • Storing vectors in databases

Vector Indexing & Search Algorithms
 • Approximate nearest neighbor concepts
 • HNSW, IVF, and PQ indexing basics
 • Index building and management
 • Search accuracy vs performance trade-offs

Popular Vector Database Platforms
 • Pinecone overview
 • Milvus overview
 • Weaviate overview
 • FAISS overview

Vector Database Architecture & Deployment
 • Standalone vs managed vector databases
 • Scaling vector search workloads
 • Storage and memory considerations
 • High availability concepts

Integration with AI & ML Pipelines
 • Using vector databases with LLMs
 • Retrieval-augmented generation basics
 • Semantic search integration
 • Recommendation system pipelines

Performance Tuning & Optimization
 • Tuning index parameters
 • Monitoring query latency
 • Balancing recall and throughput
 • Capacity planning for vector workloads

Security & Data Governance
 • Securing vector databases
 • Access control basics
 • Protecting embedding data
 • Compliance considerations

Vector Database Project Workshop & Best Practices
 • Building a semantic search application
 • Creating and querying vector indexes
 • Integrating with an AI application
 • Final workshop review and best practices

• Vector Databases Fundamentals & Vector Data Architecture
• Database Setup, Collections & Embedding Configuration
• Vector Modeling, Indexing & Similarity Search
• Query Processing, AI Integration & Semantic Retrieval
• Testing, Debugging & Performance Optimization
• End-to-End Vector Database Development Project

Participants will be evaluated through hands-on vector database labs, practical embedding and search exercises, instructor-led reviews, and a final assessment focused on building a vector-based semantic search solution.

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Upon successful completion of the training, participants will receive an AcadNXT Certificate of Completion for Introduction to Vector Databases. This digital, verifiable certification validates practical vector search, AI data integration, and modern vector database platform skills and can be shared on LinkedIn and included in professional profiles to enhance AI and data engineering career credibility.