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MLlib is known for its speed and efficiency in handling large-scale machine learning tasks, mainly due to its in-memory computation capabilities. This design allows it to perform operations much faster than disk-based systems like Mahout, which relies on disk storage for data processing, resulting in slower execution times due to the overhead associated with reading from and writing to disk.

The claim that MLlib is approximately nine times faster than disk-based Mahout stems from empirical studies and benchmarks conducted in various environments. These studies often highlight the significant performance improvements that can be achieved using in-memory processing, which minimizes the latency often seen in traditional disk-based systems. The efficient use of distributed computing resources and optimized algorithms further contribute to MLlib’s superior performance metrics compared to Mahout.

While the other figures could also reflect comparative speeds in certain contexts, the designation of nine times represents a widely accepted benchmark that illustrates the effectiveness of MLlib when processing large datasets and executing complex machine learning algorithms.