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 (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. Model artifacts are automatically captured when saved to the /models directory.