Model compression, algorithm optimization, and hardware acceleration are key to meeting the accuracy your application requires within its constraints. Model compression is used to reduce the size and complexity of machine learning models, which can help to improve the performance of the models on edge devices. Algorithm optimization is used to improve the speed of machine learning algorithms while preserving accuracy. Hardware acceleration is used to speed up the execution of machine learning algorithms on specialized hardware.