Data Science jobs enable custom tasks that you can apply any use case you have, such as data preparation, model training, hyperparameter tuning, batch inference, and so on.
Using jobs, you can:
Run machine learning (ML) or data science tasks outside of notebook sessions in JupyterLab.
Operationalize discrete data science and machine learning tasks as reusable runnable operations.
Automate typical MLOps or CI/CD pipeline.
Run batches or workloads triggered by events or actions.
Batch, mini batch, or distributed batch job inference.
Process batch workloads.
Bring your own container.
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