What are the two types of SavedModels generated by the Trainer component?
The Trainer component in TensorFlow Extended (TFX) is responsible for training machine learning models using TensorFlow. When training a model, the Trainer component generates SavedModels, which are a serialized format for storing TensorFlow models. These SavedModels can be used for inference and deployment in various production environments. In the context of the Trainer component, there are two types of SavedModels that are generated: the serving SavedModel and the evaluation SavedModel.
1. Serving SavedModel:
The serving SavedModel is designed for serving predictions in a production environment. It includes the necessary artifacts and metadata to load the model and make predictions efficiently. This SavedModel is optimized for low-latency inference and is typically used in serving systems such as TensorFlow Serving or TensorFlow Lite. The serving SavedModel contains the trained model's graph, variables, and any additional assets required for serving, such as vocabulary files or feature preprocessing logic.
For example, let's say we have trained a sentiment analysis model using the Trainer component. The serving SavedModel would include the trained model's graph, which defines the computation performed by the model, as well as the model's variables, which store the learned parameters. It would also include any additional assets, such as a vocabulary file that maps words to numerical representations. This serving SavedModel can then be loaded into a serving system, where it can accept input data and produce predictions efficiently.
2. Evaluation SavedModel:
The evaluation SavedModel is used for evaluating the performance of the trained model. It includes the necessary artifacts and metadata to perform evaluation on a different dataset than the one used for training. This SavedModel is typically used to compute evaluation metrics such as accuracy, precision, recall, or any other relevant metrics. The evaluation SavedModel contains the trained model's graph, variables, and any additional assets required for evaluation.
Continuing with the sentiment analysis example, the evaluation SavedModel would include the same trained model's graph and variables as the serving SavedModel. However, it would also include additional logic for performing evaluation on a separate dataset. This might involve loading the evaluation dataset, preprocessing the input data, and computing the desired evaluation metrics. The evaluation SavedModel allows for consistent and reproducible evaluation of the trained model's performance.
The Trainer component in TFX generates two types of SavedModels: the serving SavedModel for efficient prediction serving in production environments, and the evaluation SavedModel for evaluating the performance of the trained model on a separate dataset. These SavedModels are essential for deploying and evaluating machine learning models effectively.
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