What factor increases the resilience of distributed machine learning systems?

Fault tolerance mechanisms significantly enhance the resilience of distributed machine learning systems by ensuring that the system can continue operating smoothly even when some components fail. In distributed systems, where tasks are performed across multiple nodes, it's crucial to have strategies in place to handle failures gracefully.

For instance, if one node goes down, fault tolerance mechanisms can redirect tasks to other nodes, thereby minimizing downtime and maintaining the overall functionality of the system. These mechanisms often involve techniques such as checkpointing, which saves the state of a system at certain intervals, and replication, where data and processes are duplicated across multiple nodes.

While other factors like networking speed, data redundancy, and user interface design contribute to the effectiveness and usability of a distributed machine learning system, it is the fault tolerance mechanisms that specifically target the reliability and stability of the system under failure conditions. This capability allows machine learning workflows to be more robust and dependable in real-world applications.