hands-on lab

Tuning Hyperparameters with Hyperdrive in Azure Machine Learning

Difficulty: Intermediate

Duration: Up to 1 hour

Students: 622

Rating: 3.3/5

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Description

In the world of data science, model parameters are the elements generated from training a dataset. In contrast, a hyperparameter is a parameter used to control the outcome of training the model. Machine learning models are deep and complex and require several hyperparameters to build the best model. There is no logical formula for obtaining the best hyperparameter values. These values must be tweaked and analyzed. Like tuning a musical instrument, data scientists must tune their training models to achieve the best possible outcome.

Hyperparameter optimization can become a tedious task of tweaking values and re-running experiments. Hyperdrive is a Python package that automates this process in Azure Machine Learning. Deploying experiments with Hyperdrive dramatically reduces the process of manually tweaking the hyperparameters used for each experiment.

In this lab, you will dive into Azure Notebooks and launch a Jupyter notebook to create a Hyperdrive experiment and perform hyperparameter tuning against a regression training model.