What is distributed machine learning?

What is distributed machine learning?

Distributed machine learning is a method where multiple machines work together to train a model, improving efficiency and scalability.

How does it differ from traditional machine learning?

In traditional machine learning, a single machine processes all data, while distributed machine learning splits the data across multiple machines.

What are the benefits of using distributed machine learning?

Benefits include faster training times, the ability to handle larger datasets, and improved resource utilization.

Can you give an example of a framework used for this?

Sure! TensorFlow and PyTorch both support distributed training, allowing models to be trained across multiple devices.

What challenges might arise with distributed machine learning?

Challenges include data synchronization, network latency, and ensuring model consistency across different machines.