The Deep Learning Architect's Handbook

15

Deploying Deep Learning Models to Production

In the previous chapters, we delved into the intricacies of data preparation, deep learning (DL) model development, and how to deliver insightful outcomes from our DL models. Through meticulous data analysis, feature engineering, model optimization, and model analysis, we have learned the techniques to ensure our DL models can perform well and as desired. As we transition into the next phase of our journey, the focus now shifts toward deploying these DL models in production environments.

Reaching the stage of deploying a DL model to production is a significant accomplishment, considering that most models don’t make it that far. If your project has reached this milestone, it signifies that you have ...