Which programming framework best supports machine learning projects that involve iterative, multi-stage ML algorithms?

The best choice for supporting machine learning projects that involve iterative, multi-stage ML algorithms is Apache Spark. Spark is designed for fast and efficient data processing and is particularly well-suited for iterative algorithms used in machine learning applications.

One of the key features of Spark is its in-memory processing capabilities, which allow data to be stored in memory across various stages of computation. This significantly speeds up the performance of iterative algorithms that require multiple passes over the same dataset, such as those often found in machine learning workflows. Additionally, Spark comes equipped with MLlib, which is a library specifically built for machine learning tasks. This library includes numerous tools and algorithms for data classification, regression, clustering, and more, all of which can benefit from Spark's efficient execution model.

The other frameworks mentioned, while powerful in their own rights, do not align as closely with the needs of iterative multi-stage ML processes. For example, although Apache Hadoop excels in batch processing and managing large datasets, it is not optimized for the memory-intensive operations required by machine learning algorithms. Apache Cassandra focuses on managing large amounts of structured data across many servers but lacks the necessary support for complex iterative computations. Lastly, while Apache Flink is excellent for stream processing and event-driven applications, it is less