Summary
In this chapter, we explored the various aspects of deploying DL models in production environments, focusing on key components, requirements, and strategies. We discussed architectural choices, hardware infrastructure, model packaging, safety, trust, reliability, security, authentication, communication protocols, user interfaces, monitoring, and logging components, along with continuous integration and deployment.
This chapter also provided a step-by-step guide for choosing the right deployment options based on specific needs, such as latency, availability, scalability, cost, model hardware, data privacy, and safety requirements. We also explored general recommendations for ensuring model safety, trust, and reliability, optimizing model latency, and utilizing tools that simplify the deployment process.
A practical tutorial on deploying a language model with ONNX, TensorRT, and NVIDIA Triton Server was presented, showcasing a minimal workflow needed for accelerated deployment...
