Concurrency and Parallelism

Efficient task management is crucial in modern computing, especially when dealing with complex machine learning algorithms. Concurrency and parallelism are key concepts that allow programs to handle multiple tasks effectively, optimizing performance and resource utilization. As machine learning applications become more advanced, understanding how to leverage these techniques ensures that your Python code remains efficient and scalable.

In this chapter, you'll explore the distinctions between concurrency and parallelism, and their roles in executing multiple operations. You'll gain insights into Python's concurrency frameworks, such as threading and multiprocessing, and learn how to implement these techniques to enhance the performance of your machine learning applications. By the end of this chapter, you'll be equipped with the skills to write Python code that can handle concurrent executions, making your machine learning solutions more robust and responsive.