[Verse 1] Built your model, trained it right, now it's time to take flight From the lab to production, serving users day and night First we serialize the weights, save the state for later dates ONNX, TorchScript, SavedModel too, pick the format that's right for you [Chorus] Deploy, serve, and scale it up APIs flowing, fill the cup REST or gRPC tonight Docker containers running bright A-B testing, canary drops Blue-green switching never stops Model serving, that's the way Keep the systems live all day [Verse 2] TorchServe handles PyTorch dreams, Triton's built for inference streams vLLM for language model calls, TensorFlow Serving serves them all Design your API with care, REST endpoints everywhere WebSocket streaming real-time, gRPC when performance climbs [Chorus] Deploy, serve, and scale it up APIs flowing, fill the cup REST or gRPC tonight Docker containers running bright A-B testing, canary drops Blue-green switching never stops Model serving, that's the way Keep the systems live all day [Bridge] Kubernetes orchestrates the fleet Lambda functions serverless and sweet Edge deployment, mobile fast TensorRT makes inference last Quantization cuts the size Pruning helps the model fly Batch requests for throughput gain Knowledge distillation trains [Verse 3] Shadow mode tests without risk, canary releases bit by bit A-B testing splits the load, compare models on the road Health checks ping the service heart, monitoring right from the start Production traffic routing clean, blue-green keeps the service lean [Chorus] Deploy, serve, and scale it up APIs flowing, fill the cup REST or gRPC tonight Docker containers running bright A-B testing, canary drops Blue-green switching never stops Model serving, that's the way Keep the systems live all day [Outro] From serialized to containerized Your model's now productionized Serving users far and wide ML deployment, done with pride
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