Pre-training is typically used in the early stages of the machine learning workflow, specifically as a part of the initialization process for deep learning models. It is an unsupervised learning technique that involves training a model on a large dataset to learn general features and representations of the data before fine-tuning it on a specific task with labeled data.

  The main goal of pre-training is to enable the model to capture and extract meaningful features from the data. When there is a scarcity of labeled data, pre-training on a large dataset can help the model to learn useful representations that can be transferred to new tasks. This is particularly beneficial in domains where collecting labeled data is expensive or time-consuming.

  Pre-training is commonly employed in natural language processing (NLP) tasks, such as language modeling, text classification, and machine translation. Models like BERT, GPT, and ELMo have been pre-trained on large corpora of text data to learn contextualized representations of words and sentences. These pre-trained models can then be fine-tuned on specific downstream tasks using smaller labeled datasets.

  Additionally, pre-training can be used in computer vision tasks as well. Popular models like AlexNet, VGG, and ResNet have been pre-trained on large image datasets, such as ImageNet, to learn generic visual representations. These pre-trained models can then be fine-tuned on specific image classification, object detection, or image segmentation tasks.

  Overall, pre-training is typically used in the initial stages of the machine learning workflow to initialize deep learning models with meaningful representations learned from large unlabeled datasets. This helps to improve model performance, especially when labeled data is scarce or expensive to obtain.