Technologies / Data & Analytics

Vector Databases — Pinecone, Weaviate and ChromaDB for AI Applications

This training covers the practical use of three leading vector databases — Pinecone, Weaviate and ChromaDB — in the context of building AI applications. Participants learn the fundamentals of vector representations, similarity search mechanisms, and integration patterns with language models in RAG architectures. The program focuses on selecting the right solution and achieving production-ready deployments.

Why choose this training?

Vector databases have become a fundamental component of modern AI applications — from semantic search systems through RAG-based chatbots to recommendation engines. Choosing the right technology and implementing it correctly directly impacts the quality of language model responses and the performance of the entire system. This training gives participants the practical skills needed to consciously design and build solutions powered by vector databases.

The program covers three leading platforms — Pinecone as a managed cloud service, Weaviate as a mature open-source solution, and ChromaDB as a lightweight database ideal for prototyping. Participants not only learn the API and architecture of each tool but, more importantly, develop criteria for selecting a solution based on project requirements — data scale, budget, latency needs, and deployment model.

This training is particularly valuable for ML engineers, backend developers, and AI architects who want to move beyond simple prototypes and build production-grade RAG systems with a properly selected and optimized vector database.

What makes our approach unique?

EITT delivers over 2,500 training courses led by more than 500 experts, and our AI and data engineering programs are grounded in real-world production deployment experience. This vector databases training was developed by practitioners who design and maintain semantic search systems handling millions of queries on a daily basis.

Over 2 intensive days, participants progress from theoretical foundations — embeddings and similarity metrics — to building complete RAG pipelines with production-grade monitoring and optimization patterns. Each module includes hands-on exercises where participants work with real datasets and compare the behavior of different vector databases side by side.

Looking for a training tailored to your team’s needs? Contact us — we will prepare a program customized to your technology stack and deployment scale.

Benefits

Master the fundamentals of vector representations and similarity search mechanisms
Gain practical skills working with Pinecone, Weaviate and ChromaDB
Design and implement RAG architectures with vector databases
Make informed technology choices based on project requirements
Deploy production-ready solutions with performance, cost and security considerations

Who is this training for?

ML engineers building semantic search systems

Data engineers integrating vector databases into data pipelines

Backend developers building AI-powered applications

AI architects designing RAG solutions and recommendation engines

Technical leads evaluating vector database technologies

Prerequisites

Intermediate-level Python programming skills
Basic knowledge of machine learning and language models
Experience with SQL or NoSQL databases
Familiarity with REST API fundamentals

Training program

Embeddings and text vectorization models
Similarity metrics — cosine, euclidean, dot product
Vector indexes — HNSW, IVF, PQ
Comparing vector databases on the market
Criteria for selecting the right solution

Pinecone architecture and serverless model
Creating indexes and managing namespaces
Upsert, query and metadata filtering operations
Scaling and cost optimization
Integration with LangChain and LlamaIndex

Weaviate architecture and schema-based data model
Built-in vectorization modules
Hybrid search — vector and keyword
GraphQL API and advanced filtering
Deployment with Docker and Kubernetes

ChromaDB as an embedded database for prototyping
Collections, documents and metadata management
Integration with AI frameworks
Migrating from ChromaDB to production solutions

Retrieval-Augmented Generation architecture
Chunking and document splitting strategies
Building indexing and retrieval pipelines
Retriever quality evaluation
Advanced patterns — re-ranking, hybrid search, multi-query

Vector database performance monitoring
Index update strategies
Security and access control
Cost and latency optimization
Load testing and benchmarking

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What are the prerequisites for this training?

For the Vector Databases training we recommend: intermediate-level Python programming skills; basic knowledge of machine learning and language models; experience with SQL or NoSQL databases.

What is the format and duration of this training?

The training lasts 2 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: ML engineers building semantic search systems; data engineers integrating vector databases into data pipelines; backend developers building AI-powered applications; AI architects designing RAG solutions and recommendation engines.

Do we work with all three vector databases during the training?

Yes, the program includes hands-on workshops with Pinecone, Weaviate and ChromaDB. Participants build solutions on each platform, enabling them to compare capabilities and make an informed technology choice for their specific project requirements.

Does the training cover integrating vector databases with language models?

Yes, a dedicated module covers RAG patterns — from document chunking strategies through building retrieval pipelines to evaluating response quality. We work with LangChain and LlamaIndex frameworks in combination with each of the covered databases.