What is parallelism in data science?
An example of an AI chatbot conversation about parallelism in data science.
What is parallelism in data science?
Parallelism in data science refers to the simultaneous execution of processes to improve computational efficiency.
Can you give me an example of how it's used in machine learning?
Sure! In machine learning, parallelism can be used during model training, where multiple algorithms are trained on different data subsets at the same time.
What are the benefits of using parallelism?
The main benefits include faster processing times, improved resource utilization, and the ability to handle larger datasets.
Are there any tools that support parallelism in data science?
Yes, tools like Apache Spark, Dask, and TensorFlow support parallel processing for data science tasks.
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