Weaviate Database Mastery
About this course
This intensive course delivers end‑to‑end expertise with Weaviate, the open‑source, production‑grade vector database built for enterprise-scale and AI‑driven search. Beginning with Docker deployment, you will design flexible schemas, index heterogeneous data, and secure clusters with TLS and role‑based access. Hands‑on labs cover GraphQL and REST querying, hybrid keyword‑vector search, and multimodal pipelines that index text and images. Performance modules teach index tuning, sharding, and auto‑scaling to meet low‑latency SLAs. By the final project, you will have built a full‑stack search solution that blends precise keyword matching with semantic understanding, ready for recommendation, content discovery, or knowledge‑base use. These skills are essential for ML engineers delivering reliable, enterprise‑level search and recommendation systems.
63/100
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What you'll learn
- deploy Docker for Weaviate
- design and implement flexible data schemas
- secure clusters with TLS and role-based access
- execute GraphQL and REST queries
- perform hybrid keyword-vector searches
- optimize index performance with tuning and sharding
- build a full-stack search solution
Course objectives
- to provide comprehensive expertise in using Weaviate
- to enable the creation of scalable and secure search systems
- to integrate keyword and semantic search capabilities
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