Deploys and optimizes LLM and machine learning models for production-grade inference and API integration.
Model Serving provides a comprehensive framework for deploying Large Language Models (LLMs) and traditional ML models using industry-standard engines like vLLM, TensorRT-LLM, and BentoML. It enables developers to implement high-throughput inference, optimized GPU utilization, and streaming response patterns while integrating with orchestration tools like LangChain and LlamaIndex for RAG pipelines. Whether you are hosting Llama models locally or scaling production APIs on Kubernetes, this skill offers the implementation patterns and best practices required for efficient, low-latency AI delivery.
Key Features
01Pre-configured patterns for SSE streaming response APIs
02158 GitHub stars
03Traditional ML deployment with BentoML and Triton
04GPU memory optimization and quantization strategies
05Support for high-throughput engines like vLLM and TensorRT-LLM
06RAG orchestration with LangChain and LlamaIndex
Use Cases
01Building real-time AI chat interfaces with streaming backend support
02Self-hosting Llama, Mistral, or Qwen for private production applications
03Optimizing GPU infrastructure costs through PagedAttention and quantization
