Section 1 - Deep Learning Challenges and MLflow Prime

In this section, we will learn about the five stages of the full life cycle of deep learning (DL), and understand the emerging field of machine learning operations (MLOps) and the role of MLflow. We will provide an overview of the challenges in the four pillars of a DL process: data, model, code, and explainability. Then, we will learn how to set up a basic local MLflow development environment and run our first MLflow experiment for a natural language processing (NLP) model built on top of PyTorch Lightning Flash. Finally, we will explain the foundational MLflow concepts such as experiments, runs, and many more, through this first MLflow experiment example.

This section comprises the following chapters:

• Chapter 1, Deep Learning Life Cycle and MLOps Challenges
• Chapter 2, Getting Started with MLflow for Deep Learning