That’s such an important point  the leap from research-grade deep learning to production-ready systems is where most real challenges emerge.

In my experience, success in production isn’t just about the model  it’s about the ecosystem around it. Robust data pipelines, continuous model monitoring, and version control (for both data and weights) make a huge difference in maintaining stability.

Techniques like quantization, pruning, and ONNX optimization can help make models more resource-efficient without major accuracy loss. But the real game-changer is continuous validation  testing models on fresh, real-world data streams to catch drift early.

At the end of the day, reliable AI isn’t just well-trained it’s well-managed.
Would love to know what processes or tools others use to keep their deep learning models performing consistently once deployed.