End-to-end support for the deep learning workflow
Achieve faster time to results
Distributed training on multiple servers and GPUs includes optimized software and frameworks to accelerate training times.
Greater neural network model accuracy with hyper-parameter search and optimization, and with training visualization and tuning assistance.
Reduce time preparing data
Less time spent importing, transforming and preparing data. Use Apache Spark to manage data sources and imports.
Imporove ROI with shared resources
Better ROI with multi-tenant access to shared resources, which allow multiple data scientists to run different models at the same time on the same resources.
A consolidated framework for deep learning, monitoring and reporting enables you to achieve faster time to results with simplified management.
Add to IBM Spectrum Conductor
Add a deep learning solution to IBM Spectrum Conductor. This highly available multitenant framework is designed to build a shared, enterprise-class Apache Spark environment.
- Distributed data ingest, transformation and training
- A distributed training fabric
- Support for large models
- Helps avoid interruptions during training
- Training visualization and tuning
- Hyper-parameter search and optimization
- Technical specifications