Overview

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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.

Syllabus

Spin Up Weaviate

This module focuses on the essential first step: setting up and running your database environment. You will learn how to use Docker Compose to launch a Weaviate instance locally, understand its configuration, and define a data schema that tells the database how to structure incoming information.

Model Data in Weaviate

Model Data in Weaviate is an intermediate, project‑based course for developers and data professionals. You’ll design high‑performance, multi‑class schemas with relational links, import interconnected data, benchmark query latency, and prove performance gains, leaving you with a portfolio‑ready project and a repeatable methodology for optimizing vector‑search architecture.

Query Weaviate Smartly

Query Weaviate Smartly is an intermediate course for developers and engineers. You’ll master advanced Weaviate Python client queries for semantic, vector, and hybrid search, analyze performance traces in Weaviate Cloud, eliminate latency bottlenecks, and build faster, more relevant, production‑grade search applications.

Enable Vectorization in Weaviate

Enable Vectorization in Weaviate is an intermediate, hands‑on course for developers and ML engineers. You’ll configure Weaviate’s built‑in vectorizers (OpenAI, Cohere) in Docker, define a schema for automatic embedding, ingest data, and conduct a cost‑benefit analysis, delivering a production‑ready, efficient vector database.

Blend Hybrid Search

Blend Hybrid Search is an intermediate course for developers and ML engineers. You’ll build a search system that fuses BM25 keyword matching with dense vector semantics, tune weighting parameters, evaluate with NDCG, and create a reusable script and data‑driven methodology for maximizing relevance in any AI‑powered search application.

Unlock Multimodal Search

Unlock Multimodal Search is an intermediate, hands‑on course for developers and ML engineers. You’ll configure a Weaviate schema for image and text embeddings, ingest multimodal data, run cross‑modal queries, and measure precision, delivering a working image‑to‑text search demo and the skills to build and validate sophisticated multimodal AI applications.

GenAI Literacy: AI-Enhanced Search Optimization

This module teaches search engineers how to leverage generative AI to accelerate Weaviate development. Learners will generate complex GraphQL queries, obtain AI‑suggested hybrid‑search parameters, and debug performance issues—while applying expert prompting patterns and rigorously validating every AI‑generated suggestion for correctness and production‑readiness.

Enterprise Multimodal Search Engine

In this Module, Enterprise Multimodal Search Engine project, you’ll design and implement a production‑ready Weaviate search API that handles text, image, and multimodal queries with hybrid scoring, integrating schema design, vectorization, and systematic parameter tuning. Building on Short Courses 3.1–3.6 (and optionally Long Course 2), you’ll evaluate search quality (Precision@5, NDCG@5) and submit an AI‑graded, portfolio‑ready deliverable that meets real‑world enterprise requirements.