How to feed model output data back for training?

Course Q&A Machine Learning in Production
1

I have come across the idea of feeding back model output data to enhance training in ML courses including this one (MLEP). Curious to know how to implement this feedback in practical systems?

2

Hello @chetna and welcome to DeepLearning.AI community.
The method of feeding model output data back for training is commonly used in scenarios where you have a machine learning model that is making predictions, but its accuracy is not satisfactory, and you want to improve its performance. Here are some common scenarios where this method can be used:

  1. Image classification: In image classification, you can train a model to classify images into different categories. However, if the model is making incorrect predictions, you can collect feedback data on its predictions and use that data to retrain the model.
  2. Natural language processing: In natural language processing, you can train a model to perform tasks such as sentiment analysis or named entity recognition. If the model is not performing well, you can use feedback data to retrain the model.
  3. Recommendation systems: In recommendation systems, you can train a model to make personalized recommendations to users. If the model is not making accurate recommendations, you can use feedback data to retrain the model.
  • Here are the steps involved in this process:
  1. Collect labeled data: First, you need to collect data that has already been labeled or annotated by humans. This data should consist of input-output pairs, where the input is the data that you want to process, and the output is the expected result.
  2. Train the model: Next, you need to train your model using the labeled data you have collected. During the training process, the model will learn to map the input data to the correct output data.
  3. Generate predictions: Once your model is trained, you can use it to make predictions on new, unseen data.
  4. Collect feedback data: Collect feedback data on the predictions made by your model. This feedback data should consist of input-output pairs, where the input is the data that your model made a prediction on, and the output is the correct result. For example, if your model is a image classifier, the feedback data could consist of images and their correct classifications.
  5. Use the feedback data to retrain the model: Finally, you can use the feedback data to retrain your model. This will help the model learn from its mistakes and improve its predictions. You can repeat this process iteratively to continuously improve the performance of your model.