Building end-to-end machine learning (ML) pipelines is essential for modern ML engineers to enhance efficiency and reduce errors in model deployment. These pipelines automate and orchestrate the various stages of ML workflows, including data acquisition, model development, and model management, ensuring reproducibility, scalability, and integration with external systems. ML pipelines consist of three main types: data pipelines, model training pipelines, and serving pipelines, each addressing specific workflow stages. The process involves defining modular components, containerizing them, and using orchestration tools like Kubeflow, Metaflow, or ZenML to manage the workflow. Challenges such as infrastructure demands, complex interdependencies, and ensuring reproducibility are common, but best practices like experiment tracking, modular component design, and thorough testing can mitigate these issues. Additionally, tools like Neptune and MLflow aid in monitoring and tracking pipeline performance, contributing to more reliable and efficient ML operations.