model serving
AI, ML & GenAI 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 divides working systems from broken ones is train/serve consistency: the exact resize, color space, and normalization have to match at training and inference. AI, ML, and GenAI engineer interviews probe this because most CV failures live at the preprocessing seam, not in the architecture.📊 Evaluation & ML FoundationsSign in
Core
Model Serving FrameworksYou seldom build a serving stack from scratch; frameworks take care of the production plumbing. General servers (Triton, TorchServe, KServe) host many model types with dynamic batching, multi-model hosting, and versioning. LLM-specific servers (vLLM, TGI, TensorRT-LLM) add the essentials general servers miss: continuous batching, paged KV cache, and token streaming. AI, ML, and GenAI engineer interviews probe it because knowing what these provide, and that LLM serving needs the specialized ones, is practical deployment knowledge.🖥️ ML Infrastructure & ServingSign in