MLOps & Infrastructure · intermediate · concept 200 of 311 · Updated Nov 4, 2026
Model Deployment & Serving
Getting trained models into production where they serve real users. Involves containerization (Docker), API frameworks (FastAPI), model servers (TensorRT, vLLM), and monitoring. Serving splits two ways: real-time endpoints optimized for latency, and batch jobs optimized for throughput and cost.
Top AI contributors on this
- Jensen HuangNvidia
- Jeff DeanDiscovery Loop
- Clément DelangueHugging Face
- Satya NadellaMicrosoft
- Sundar PichaiAlphabet / Google
- Alexandr WangMeta
Key terms
DockerFastAPITensorRTvLLMLatencyThroughputTriton & inference serversBatch vs real-time
Learn these first
Videos
▶ Accelerating model deployment with MLOps ↗
Google Cloud Tech · YouTube
▶ What is MLOps? ↗
IBM Technology · YouTube
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