Vector Optimization for LLMs 301
VEC-301Master enterprise-scale vector search with advanced multi-modal embeddings, distributed architectures, and optimization techniques. Build production-ready LLM retrieval systems that scale.
🚀 Build Enterprise-Grade Vector Search Systems
Take your LLM applications to the next level with Vector Optimization for LLMs 301—an advanced, hands-on workshop designed for AI engineers ready to tackle real-world scale. Master the art of building multi-modal retrieval systems that handle massive data volumes while maintaining lightning-fast performance and accuracy.
💡 What Makes This Training Essential
In this intensive workshop, you'll go beyond basic vector search to implement production-ready solutions using cutting-edge techniques:
- 🎯 Multi-Modal Integration: Combine text, images, and structured data into unified search pipelines
- ⚡ Advanced Optimization: Apply PCA, quantization, and compression to reduce storage costs by up to 75%
- 📈 Enterprise Scaling: Deploy distributed FAISS and Milvus clusters for billion-scale indexing
- 🎓 Precision Reranking: Implement cross-encoder models to boost retrieval accuracy
- 🔄 Lifecycle Management: Monitor embedding drift and automate refresh workflows
🛠️ Hands-On Labs & Real-World Applications
Five comprehensive labs guide you through building, optimizing, and scaling production systems. You'll work with industry-standard tools including Jupyter, HuggingFace, FAISS, and Milvus. By course end, you'll have the expertise to design retrieval architectures that balance cost, performance, and accuracy for enterprise deployments.
Perfect for ML engineers, data scientists, and AI architects building next-generation search and RAG applications.