What is distributed computing in machine learning?

What is distributed computing in machine learning?

Distributed computing in machine learning refers to the use of multiple computers to process data and train models simultaneously, improving efficiency and scalability.

Why is it important for large datasets?

It's crucial because large datasets can be too big for a single machine to handle. Distributed computing allows for parallel processing, which speeds up training and analysis.

Can you give me an example of how it's used?

Sure! For instance, companies like Google use distributed computing for training their deep learning models across thousands of machines, enabling them to process vast amounts of data quickly.

What are some challenges associated with it?

Some challenges include data synchronization, network latency, and ensuring fault tolerance, as failures in one part of the system can affect the entire process.