Optimized software reduces the time spent importing, transforming and preparing data. Distributed training on multiple servers and GPUs speeds time to results.
Hyper-parameter search and optimization, and training visualization and tuning enable greater model accuracy.
Shared resources among many data scientists running different models drives higher utilization.
This highly available multitenant framework is designed to build a shared, enterprise-class Apache Spark environment.
When optimized to take full advantage of IBM Power Systems™ Servers with NVLink CPUs and NVIDIA GPUs, IBM benchmarks have seen 50x improvements, cutting training times from days to hours.