IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Serving ML Models with FastAPI and Docker
Putting a model behind an API is where many projects stall. This guide builds a FastAPI prediction service that loads a model once at start-up, enforces an input contract, serves single and batch predictions, reports health and readiness separately, and shows the Dockerfile that packages it.
vLLM and Model Serving: Scaling LLM Inference
Running a model for one user is easy. Serving it to hundreds at once is a scheduling and memory problem. This guide explains decode steps, batching and the key-value cache, simulates why continuous batching keeps a GPU busy, and shows how vLLM is started and called through an OpenAI-compatible API.