What is an important library used for distributing traditional machine learning tasks?

Spark ML is a key library designed specifically for distributing traditional machine learning tasks across a cluster. It builds on the resilience and scalability provided by Apache Spark, allowing users to handle large datasets that may not fit into memory on a single machine. This capability is crucial for tasks that require the processing of big data, leveraging distributed computing to train models efficiently.

Additionally, Spark ML provides a consistent set of APIs, integrates seamlessly with existing Spark data processing workflows, and aligns well with Spark's architecture for data manipulation and analysis. This makes it particularly useful in scenarios where data processing and machine learning need to occur together.

The other libraries, while valuable in their domains, focus on different aspects. TensorFlow and Keras are primarily geared towards deep learning and neural networks, rather than traditional machine learning. Scikit-learn is an excellent library for traditional machine learning tasks, but it is not designed for distributed computing and would perform poorly on very large datasets compared to Spark ML.