Chapter 2: Getting Started with MLflow for Deep Learning
One of the key capabilities of MLflow is to enable Machine Learning (ML) experiment management. This is critical because data science requires reproducibility and traceability so that a Deep Learning (DL) model can be easily reproduced with the same data, code, and execution environment. This chapter will help us get started with how to implement DL experiment management quickly. We will learn about MLflow experiment management concepts and capabilities, set up an MLflow development environment, and complete our first DL experiment using MLflow. By the end of this chapter, we will have a working MLflow tracking server showing our first DL experiment results.
In this chapter, we're going to cover the following main topics:
- Setting up MLflow
- Implementing our first MLflow logging-enabled DL experiment
- Exploring MLflow's components and usage patterns
