# 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](/wiki/generative-adversarial-network-gan.md) (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="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/artifacts.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.