An artifact is a machine learning term that is used to describe the output created by the training process. Output could be a fully trained model, a model checkpoint, or a file created during the training process.
AI Artifacts describe all digital products that are used in an AI Tool. They can be the input, the output, or an intermediate result that is processed by tools. We mainly specify six types of artifacts(corresponding to the steps in the pipeline): Data, Knowledge, Model, Application, Algorithm, and Benchmark.
An artifact is a byproduct of software development that helps to describe the architecture, design, and function of the software. Artifacts act like roadmaps that software developers can use to trace the entire software development process. These artifacts may be databases, data models, printed documents, or scripts.
Artifacts also describe all the digital assets used in an artificial intelligence tool. Examples include the input, the output, or an intermediate result that is processed by tools.
H2O Driverless AI provides several configuration options/environment variables that enable exporting of artifacts instead of downloading. Artifacts can be exported to a file system directory, an Amazon S3 bucket, a Bitbucket repository, or Azure Blob storage.