Building effective machine learning pipelines often involves handling large datasets and managing complex workflows. Standard Python is capable, but certain advanced features significantly improve efficiency and maintainability. This chapter concentrates on these Python constructs specifically applied to ML pipelines.

You will learn to implement memory-efficient data handling using advanced generator techniques and coroutines. We will cover the use of context managers for reliable resource management in pipeline stages, such as handling files or model connections. Additionally, you'll see how functional programming patterns like map, filter, and the application of higher-order functions and closures can produce clearer and more reusable data transformation code. We will also work with iterators and the itertools module for sophisticated sequence manipulation. Finally, you'll put these concepts into practice by constructing a data pipeline component.