After model training, performance metrics are calculated and provided in an evaluation report. All of the information, that is the model configuration, the learned model and the evaluation report are stored in the a versioned model repository for analysis and deployment. The model information contains:
TFX uses TensorFlow as its model description. TFX has this notion of ‘warm-starting’ that is inspired by transfer learning technique found in Deep Learning. The idea is to reduce the amount of training by leveraging existing training. Unlike transfer learning that employs an existing pre-trained network, warm-starting selectively identifies a general features network as its starting point. The network that is trained on general features are used as the basis for training more specialized networks. This feature appears to be implememented in TF-Slim.