Deep learning models can achieve incredible results in research, but production environments are a different story.

Models can be sensitive to small changes in data, require massive computational resources, and sometimes behave unpredictably when faced with real-world scenarios.

I’m curious about the strategies you have found effective for handling these challenges whether it’s optimizing performance, managing data quality, or ensuring reliability.

Sharing your experiences can help the community build more robust and practical deep learning solutions.

Deep learning has incredible potential, but working with it in practice often comes with hurdles from preparing large, clean datasets to choosing the right architecture,

tuning hyperparameters, or making sure the results are interpretable.

Even when models perform well in theory, translating that into real-world impact can be tricky.

Curious to hear from the community: what challenges have you faced, and what strategies or approaches have helped you overcome them?