Best Practices for Building and Maintaining Deep Learning Models with Keras

Keras
1

Hi Keras community,

I have been exploring different approaches for building deep learning applications with Keras and looking into how developers structure their workflows for production-ready models.

While creating a model is straightforward, maintaining performance and reliability over time brings additional challenges:

  • Choosing the right model architecture
  • Managing training experiments and hyperparameters
  • Handling model evaluation and versioning
  • Optimizing inference performance
  • Deploying models into real-world applications

I would like to learn from developers working with Keras:

  1. What practices help you improve model development workflows?
  2. How do you manage experiments and compare different model versions?
  3. What tools or approaches do you recommend for deploying Keras models at scale?

Would love to hear practical experiences and lessons learned from the community.