Part 6/13:
Framework Compatibility: Models might originate from various ecosystems (PyTorch, TensorFlow, ONNX, scikit-learn). Serving infrastructure must support this heterogeneity.
Real-Time & Streaming Inference: Many applications demand near-instant responses, especially for speech recognition or dialogue systems, which necessitate optimized, low-latency serving.
Batching & Scalability: Handling high concurrency demands intelligent batching to maximize GPU throughput, reducing idle times and improving efficiency.
Deployment Environments: Cloud, on-premises, edge, or embedded devices each present distinct constraints and requirements.