3 posts
What is model serving? A guide to the serving layer: from managed APIs to self-hosted inference servers like vLLM, TGI and Ollama; batching, streaming, concurrency, throughput and latency trade-offs.
One of the most common mistakes in deep learning projects is assuming that a model with strong training metrics is ready for production. In reality, high accuracy, low loss, or strong validation performance do not guarantee readiness under real user traffic, distribution shift, latency constraints, security requirements, observability needs, failure handling, version control, or operational sustainability. Production success depends not only on model architecture, but also on data pipelines, inference design, model packaging, serving infrastructure, monitoring, rollback strategy, evaluation discipline, governance, and workflow integration. This guide explains why moving from training to production in deep learning projects requires much more than a good model, and what a production-grade AI system actually needs.
Production-grade AI systems require far more than choosing a model or framework. Real success depends on how well orchestration, deployment, observability, evaluation, security, and governance layers work together. This guide compares the core layers of the AI engineering stack, explains what each layer is responsible for, where teams make the wrong architectural decisions, and how organizations can build a more reliable and scalable AI operating model.