Deploy Production
LLMs at Scale

Learn to design and deploy production-ready LLM APIs with proper validation, error handling, and guardrails. Select and serve open-source models from Hugging Face with flexible prompt templating systems.

Build comprehensive evaluation frameworks for LLM systems. Learn rules-based testing, LLM-as-judge evaluation, and A/B testing methodologies to continuously assess and improve model performance.

Master advanced Retrieval-Augmented Generation with hybrid search, semantic chunking, and reranking. Build RAG pipelines that go beyond basic vector search with continuous evaluation and production-ready architecture.

Apply parameter-efficient fine-tuning techniques (LoRA/QLoRA) to customize models for specific use cases. Learn data preparation, hyperparameter tuning, and how to evaluate fine-tuned models against base models.

Containerize LLM applications with Docker and deploy to cloud serverless platforms. Learn image optimization, secrets management, environment separation, and basic CI/CD pipelines for automated deployment.

Optimize LLM inference for production workloads. Master caching strategies, dynamic batching, quantization, and cost modeling to balance latency, throughput, and operational expenses.

Build enterprise-ready LLM systems with comprehensive observability, security controls, and incident response capabilities. Learn to monitor, version, and secure production LLM deployments.