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SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture
The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures.
Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance
However, the parameter server architecture may have limitations in terms of fault tolerance, including a single point of failure and complex recovery processes.
Toward a traceable, explainable, and fairJD/Resume recommendation system
Typically, pre-trained language models use transfer-based machine learning models to be fine-tuned for a specific field.
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