Managing the end-to-end lifecycle of a Machine Learning application can be a challenging endeavor for data scientists, engineers, and developers. Machine Learning applications are known for their complexity, making them difficult to track, reproduce, and deploy. This course aims to address these challenges by introducing MLflow, a tool designed to simplify the Machine Learning lifecycle. By taking this course, you will gain a comprehensive understanding of MLflow and its role in overcoming the complexities associated with building Machine Learning applications. You will learn how to navigate different stages of the Machine Learning lifecycle and effectively manage the challenges that arise. Throughout the course, you will delve into the four key components of the MLflow platform. You will explore MLflow Tracking, which enables you to track models, metrics, and parameters. Additionally, you will learn how to package reproducible ML code using MLflow Projects, create and deploy models using MLflow Models, and store and version control models using Model Registry. As you progress, you will also acquire best practices for versioning models, evaluating models, adding customizations, and incorporating automation into training runs using MLflow. By the end of the course, you will be well-prepared to successfully manage the lifecycle of your next Machine Learning application. In summary, this course offers a comprehensive exploration of MLflow and its capabilities in simplifying the complexities of the Machine Learning lifecycle. It equips you with the knowledge and skills necessary to effectively manage and overcome the challenges associated with building and deploying Machine Learning applications.