Chroma, Weaviate & Production RAG Deployment

Chroma, Weaviate & Production RAG Deployment equips developers and ML engineers with end‑to‑end skills to deploy and manage vector databases for advanced search and retrieval‑augmented generation. You’ll start by launching a local Chroma instance via its Python SDK, configuring collections and ingesting thousands of documents. You’ll build automated pipelines that link embedding models (OpenAI, HuggingFace) to Chroma and troubleshoot dimension mismatches. Next, you’ll design a RAG pipeline with Chroma and LangChain to ground LLM responses in verifiable data and assess its impact. Through courses on Weaviate you’ll model complex data with multi‑class schemas, import interconnected objects, benchmark query latency and write semantic, vector and hybrid queries. You’ll spin up Weaviate with Docker Compose, define a schema and perform your first semantic search. Additional modules teach you to build a semantic search API with Chroma and Flask, manage metadata and multi‑collections via an ETL pipeline, and implement advanced RAG patterns (Corrective, Self‑RAG and Agentic). You’ll enable Weaviate’s automatic vectorization and evaluate the tradeoffs, tune index parameters to reduce latency and script migrations from Chroma to Weaviate, and deploy vector databases securely with TLS, RBAC and Grafana monitoring. By the end you’ll be ready to build, tune and maintain production‑ready vector search and RAG systems

• Deploy and configure local and cloud vector databases using Chroma and Weaviate, ingest and organize data, and perform semantic queries
• Integrate embeddings and build retrieval‑augmented generation pipelines, troubleshoot vectorization errors and evaluate RAG effectiveness
• Model and manage data structures, optimize queries and indices, migrate vectors and secure production vector databases with TLS, RBAC and monitoring

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work