Machine learning workflows, from data preprocessing to model training, frequently involve significant computation or I/O operations that benefit from concurrent execution. While Python's Global Interpreter Lock (GIL) presents challenges for certain types of parallelism, various techniques allow for substantial performance improvements.

This chapter focuses on implementing concurrent and parallel solutions in Python specifically for ML tasks. We will examine the differences and use cases for threading and multiprocessing, utilize the concurrent.futures module for simplified management, explore asyncio for I/O-heavy workloads, and address essential concepts like inter-process communication, synchronization, and debugging strategies for concurrent code. By the end of this chapter, you will be equipped to select and apply appropriate concurrency models to accelerate your Python-based machine learning applications.