Qdrant - Vector Database & Search Engine Courses

The Bitter Lesson of Retrieval in Generative Language Model Workflows

Discover how leveraging data and compute in RAG architectures can enhance generative AI applications, with insights from YOKOT.AI's implementation for enterprise data solutions.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

How Vector Search with Qdrant Improves Workplace Efficiency

Discover how vector search technology and Qdrant power modern work environments, exploring implementation, scaling challenges, and practical applications in AI-driven business solutions.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

Indexify Unveiled: A Scalable Content Extraction Engine for Multimodal Data

Discover how Indexify enables scalable content extraction and real-time knowledge base creation for AI workflows, focusing on unstructured data processing and hybrid search capabilities.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Insights from Diverse Projects in Image Search and RAG

Explore practical insights from real-world vector search projects, covering image matching, property deduplication, and RAG implementations, with focus on DinoV2 and Ada-2 model performance.

• YouTube
• 40 minutes
• Self-Paced
• Free Video

Superpower Your Semantic Search Using Vector Database - From Model Architecture to Deployment

Discover how to enhance matching systems using vector databases, focusing on implementation, deployment, and optimization for improved search performance and scalability in production environments.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

High-Performance Entity Matching Solution Using Vector Embeddings and AWS

Discover how to build a high-performance hotel matching system using vector embeddings, AWS infrastructure, and Qdrant for efficient data processing and improved accuracy in the travel industry.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

FastEmbed - Fast and Lightweight Embedding Generation

Discover how to generate fast, efficient embeddings using FastEmbed library, exploring quantized models and ONNX Runtime for improved throughput and latency in Python applications.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Cohere Embed V3: Innovations in Content Quality and Compressed Embeddings

Discover state-of-the-art embedding models featuring content quality assessment and vector compression techniques, with practical insights on implementation and scaling in vector database setups.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

AI-Powered Music Recommendations Based on Mood and Vibe

Discover how AI and vector embeddings can revolutionize music recommendations by analyzing song vibes and moods through LLMs and transformer models for more personalized listening experiences.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Binary Quantization Methods for Vector Database Optimization

Dive into vector database optimization through binary quantization, exploring compression techniques, search speed improvements, and practical implementation strategies for efficient vector similarity search.

• YouTube
• 21 minutes
• Self-Paced
• Free Video

Building a Semantic Search App for Music Using Qdrant Cloud

Learn to develop a semantic search application using Qdrant Cloud's Python SDK to efficiently search through thousands of songs across different music genres.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Music Recommendation System with Qdrant Vector Search and Audio Embeddings

Learn to build a music recommendation engine using Qdrant's vector search capabilities, audio embeddings, and metadata to create personalized song suggestions.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

Image Classification with Qdrant Vector Semantic Search for Medical Diagnosis

Learn to build an image-based semantic search engine for skin condition diagnosis using Qdrant, covering embedding generation and vector database implementation.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Natural Language Processing with Qdrant for Vector Similarity Search

Master vector similarity search using Qdrant to create embeddings from text data, perform contextual searches, and build recommendation systems for enhanced natural language processing applications.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

Getting Started with Qdrant - Introduction to Vector Database and Similarity Search

Master vector similarity search using Qdrant to build semantic search and recommendation systems through hands-on practice with sample datasets and practical implementations.

• YouTube
• 24 minutes
• Self-Paced
• Free Video