Model Deployment 🚀
Learn about the best practices, tools, and platforms for deploying Large Language Models (LLMs) into production environments.
On-Premise Deployment
Deploy models on your local servers or internal cloud infrastructure.
Cloud-Based Deployment
Leverage cloud providers like AWS, GCP, and Azure to scale LLM deployments.
Model Serving
Use frameworks like TensorFlow Serving or TorchServe to serve LLMs efficiently.
Containerization & Docker
Containerize LLM deployments for portability and ease of use with Docker and Kubernetes.
Scalability & Load Balancing
Implement horizontal scaling and load balancing for handling high-traffic LLM services.
Edge Deployment
Deploy models to edge devices for low-latency inference at the edge of the network.
Model Optimization for Deployment
Optimize LLMs for production deployment using quantization, pruning, and distillation.