Checkpointing for Resiliency and Performance in AI Pipelines

Today’s organizations are increasingly tasked to bring AI technologies to market quickly and predictably. To be successful, there needs to be a highly performant and reliable supporting architecture for their AI initiatives. Beyond just infrastructure, the technique that has become dominant to assist with maintaining resiliency in AI/ML is checkpointing. WEKA's ability to have high performance checkpointing across any model size allows for more checkpoints to be taken during model training. This ensures faster re-start of training when various failure events occur, resulting in less impact to GPU utilization, less downtime in training and less disruption to model developers and data scientists.