What is the difference between parallelism and distributed learning?

What is the difference between parallelism and distributed learning?

Parallelism refers to executing multiple operations simultaneously within a single system, while distributed learning involves multiple systems working together to learn from data.

Can you give me an example of each?

Sure! In parallelism, a single machine might use multiple cores to process data faster. In distributed learning, several machines might collaborate to train a model on a large dataset.

Which one is more efficient for large datasets?

Distributed learning is generally more efficient for large datasets, as it can leverage the power of multiple machines to handle the data more effectively.

Are there any downsides to distributed learning?

Yes, distributed learning can introduce complexities such as communication overhead, synchronization issues, and challenges in managing data across different systems.