Implementing a deep learning model is an iterative process. Continuing to refine your production model is part of the life cycle.
When you put a model into production, it’s a good idea to let users identify misclassified data as they send in data and get predictions. Creating a method to track misclassified data means that you can quickly get a model up and running. You can then continue to improve the model as you learn more about how it’s used and performs.
Implement a feedback loop in your apps with the feedback APIs. The high-level process includes these steps.
Build functionality in your app to identify image or text data that was misclassified.
Add the data, along with the correct label, to the dataset. Note that you add the feedback image or text to the dataset from which the model was created.
Train or retrain the dataset, and use the
Use the new or updated model after the feedback is incorporated.
Updated about 2 years ago