Chapter 4: Experiment Management in MLflow
In this chapter, we will give you practical experience with stock predictions by creating different models and comparing metrics of different runs in MLflow. You will be guided in terms of how to use the MLflow experiment method so that different machine learning practitioners can share metrics and improve on the same model.
Specifically, we will look at the following topics in this chapter:
- Getting started with the experiments module
- Defining the experiment
- Adding experiments
- Comparing different models
- Tuning your model with hyperparameter optimization
At this stage, we currently have a baseline pipeline that acts based on a naïve heuristic. In this chapter, we will add to our set of skills the ability to experiment with multiple models and tune one specific model using MLflow.
We will be delving into our Psystock company use case of a stock trading machine learning platform introduced in...
