model serving
Applied AI interview questions tagged model serving, across every topic.
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Concepts behind "model serving"
The curriculum that explains the ideas these questions test.
Core
The Computer Vision PipelineA production CV system is a chain: ingest and version images, preprocess and augment, fine-tune a pretrained backbone, attach a task head, evaluate with sliced metrics, post-process, then serve and monitor. The invariant that separates working systems from broken ones is train/serve consistency: the exact resize, color space, and normalization must match at training and inference. Applied AI interviews probe this because most CV failures live at the preprocessing seam, not in the architecture.📊 Evaluation & ML FoundationsSign in
Core
Model Serving FrameworksYou rarely build a serving stack from scratch; frameworks handle the production plumbing. General servers (Triton, TorchServe, KServe) serve many model types with dynamic batching, multi-model hosting, and versioning. LLM-specific servers (vLLM, TGI, TensorRT-LLM) add the essentials general servers lack: continuous batching, paged KV cache, and token streaming. Applied-AI interviews probe it because knowing what these provide, and that LLM serving needs the specialized ones, is practical deployment knowledge.🖥️ ML Infrastructure & ServingSign in