Kickstart your journey into vector databases with Multisoft Systems. Understand embeddings, similarity search, ANN indexing, and real-time retrieval powering today’s AI systems. This course is ideal for developers, data engineers, and innovators seeking practical skills to build intelligent, scalable, and efficient vector-powered search and recommendation solutions.
The Introduction to Vector Databases training by Multisoft Systems is designed to help learners understand the modern data architecture powering advanced AI applications, including semantic search, large language models, recommendations, vision systems, and intelligent agents. Traditional databases struggle to store and retrieve high-dimensional data such as embeddings generated by AI models. Vector databases solve this challenge by enabling fast, accurate similarity search using mathematical distance measures and optimized indexing techniques. In this course, learners explore the fundamentals of embeddings, vector representations, and how models like BERT, GPT, and image encoders generate meaningful numerical vectors. You will gain practical knowledge of similarity metrics, approximate nearest neighbor (ANN) algorithms, and indexing approaches such as HNSW, IVF, and PQ. The training also introduces the internal architecture of leading vector databases, including Pinecone, Milvus, Weaviate, FAISS, and Qdrant.
Through hands-on examples and real-world use cases, participants learn how to build scalable vector search systems, integrate them with AI pipelines, and deploy them in production environments. Whether you are a developer, data engineer, or AI practitioner, this course equips you with essential skills to design intelligent, high-performance applications using vector databases—an indispensable component in today’s generative AI landscape.