Machine learning workflows thrive on flexibility and performance, and PyTorch stands out by offering dynamic computational graphs while also enabling production-ready deployment through TorchScript. TorchScript leverages Just-In-Time (JIT) compilation, employing two key mechanisms—tracing and scripting—to convert Python-centric models into a serialized format. This approach bridges the gap between rapid prototyping and deployment, ensuring scalability, efficiency, … Continue reading Tracing and Scripting in ML Workflows: PyTorch, TorchScript, and JIT-ed Models