HYPERPARAMETER TUNING

Automatic Model Tuning and Evaluation

A crucial part of the model development process is hyperparameter tuning. Hyperparameters of an algorithm are at least as important as the algorithm itself and usually, data scientists spend a lot of time on this part of development.

Accelerate model development

Layer speeds up the development process by reducing the manual effort spent by the data scientist and then parallelizing the execution of this request.

Observable Hyperparameter Tuning.

Hyperparameter tuning doesn't have to be a black box. With Layer, you can monitor iterations of your hyperparameter tuning with all of its parameters and metrics.

Declarative & Expressive

Layer provides multiple searching strategies but abstracts away the implementation details. You declaratively tell Layer what you want, and it handles the how.

Reliable, Scalable Tuning

Asynchronous Jobs

Pass your hyperparameter definitions to Layer. It will build a reliable and scalable pipeline automatically so that you can keep your focus on business while it tunes your model

Make data scientists more productive

Controlled parallelization.

Hyperparameter tuning potentially requires many runs of a model to identify the strongest one. Layer handles all the complexity while providing transparent visibility of the costs.

Empower your data team

Illo

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