What are some practical applications of Dask?
Dask is great for parallel computing and can be used in data analysis, machine learning, and big data processing.
Can you give me an example of how Dask is used in machine learning?
Sure! Dask can be used to scale machine learning workflows by distributing tasks across multiple cores or machines, allowing for faster model training.
What are the benefits of using Dask over other frameworks?
Dask is flexible, integrates well with existing Python libraries, and can handle larger-than-memory datasets efficiently.
Is Dask suitable for real-time data processing?
Yes, Dask can be used for real-time data processing, especially when combined with streaming libraries like Kafka.
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