docs/examples/ml_training.rst - burr - Git at Google

 ====================
 ML Model Training
 ====================

 Hyperparameter Optimization is a key part of training machine learning models. For use-cases ranging from grid-search
 to gaussian processes, `burr` is a simple way to implement robust, failure-resistant hyperparameter optimization. This involves keeping
 track of hyperparameters and metrics and feeding these into a decision function to determine where to look next.
 ``burr``'s state machine can help you write and manage that process.

 For more information/contribution options, see the placeholder/example sketch in the `repository <https://github.com/DAGWorks-Inc/burr/tree/main/examples/ml-training>`_.
	====================
	ML Model Training
	====================

	Hyperparameter Optimization is a key part of training machine learning models. For use-cases ranging from grid-search
	to gaussian processes, `burr` is a simple way to implement robust, failure-resistant hyperparameter optimization. This involves keeping
	track of hyperparameters and metrics and feeding these into a decision function to determine where to look next.
	``burr``'s state machine can help you write and manage that process.

	For more information/contribution options, see the placeholder/example sketch in the `repository <https://github.com/DAGWorks-Inc/burr/tree/main/examples/ml-training>`_.