Chapter 13: Tracking Hyperparameter Tuning Experiments
Working with a lot of experiments can sometimes be overwhelming. Many iterations of experiments will need to be done. It will become even more complicated when we are experimenting with many ML models.
In this chapter, you will be introduced to the importance of tracking hyperparameter tuning experiments, along with the usual practices. You will also be introduced to several open source packages that are available and learn how to utilize each of them in practice.
By the end of this chapter, you will be able to utilize your favorite package to track your hyperparameter tuning experiment. Being able to track your hyperparameter tuning experiment will boost the effectiveness of your workflow.
In this chapter, we will cover the following topics:
- Revisiting the usual practices
- Exploring Neptune
- Exploring Scikit-Optimize
- Exploring Optuna
- Exploring Microsoft NNI
- Exploring MLflow