Expert insights on multi-agent systems, RAG pipelines, LLM deployment, fine-tuning, AI safety, and production AI infrastructure
Learn how to architect, build, and deploy multi-agent AI systems that work reliably in production — from agent orchestration patterns to tool use, memory, and safety guardrails.
A practical guide to building production-grade RAG pipelines with vector databases, semantic chunking, hybrid search, and evaluation frameworks that actually work at scale.
Master LLM fine-tuning techniques: LoRA, QLoRA, RLHF, and DPO. Learn when to fine-tune vs. use RAG, data preparation best practices, and production optimization strategies.
Implement comprehensive AI safety for production systems: output filtering, prompt injection defense, bias detection, content moderation, red-teaming, and continuous safety monitoring.
Compare vLLM, TGI, and Triton for LLM serving in production. Learn deployment patterns, auto-scaling strategies, batching optimizations, and performance tuning techniques.
Build and manage GPU clusters for AI workloads: Kubernetes orchestration with NVIDIA device plugin, GPU scheduling strategies, cost optimization, and MLOps best practices.
Build enterprise-grade RAG systems: vector database selection (Qdrant, Weaviate, pgvector), chunking strategies, embedding model selection, hybrid search, and production patterns.
Design patterns for agentic AI: ReAct, plan-and-execute, tool use, memory systems, and multi-agent coordination for building autonomous business workflows.