# Artifacts Artifacts is common ML term used to describe the output created by the training process. The output could be a fully trained model, a model checkpoint (for resuming training later), or simply a file created during the training process such as an image generated while training a [Generative Adversarial Network](https://machine-learning.paperspace.com/wiki/generative-adversarial-network-gan) (GAN). In the case of a Deep Learning model, the model artifacts are the trained weights stored in a binary format. ## Artifacts + Gradient Gradient makes artifact management seamless and intuitive. Anything saved in the `/artifacts` directory will be automatically captured in Gradient as an [artifact](https://docs.paperspace.com/gradient/data/storage#artifact-storage). Model artifacts are automatically captured when saved to the `/models` directory. #### Related Materials {% embed url="" %}