HPE Scaling AIMove AI from pilots to production without the infrastructure rebuilds.
HPE Scaling AI combines simplified infrastructure, pre-built integrations and operational services that reduce the time from AI proof-of-concept to production from months to weeks. The platform supports GPU-accelerated machine learning and traditional HPC workloads, eliminating the need for separate infrastructure for AI and conventional compute.
- Deployed in 200+ global organisations scaling AI workloads; 50+ UK deployments
- Reduces time-to-production by 50-60% compared to building infrastructure from components
- Optimised for NVIDIA, AMD and Intel AI processors; runs open-source ML frameworks natively
- Full UK compliance with FCA, NHS Digital and data residency requirements
HPE AI-Ready Systems with NVIDIA Integration
HPE’s AI infrastructure comes pre-optimised with NVIDIA GPUs, high-speed interconnect and balanced memory architecture. Systems arrive configured for machine learning frameworks (TensorFlow, PyTorch), eliminating weeks of integration work. Teams deploy models immediately rather than spending time tuning infrastructure. Available as on-premises systems or integrated with public clouds.
HPE Swallow Data Fabric
Data preparation consumes 60-70% of machine learning projects. Swallow provides automated data discovery, quality assessment and preparation pipelines that run in parallel with model training. It connects to data sources across the organisation, standardises data formats and creates reusable datasets for multiple AI projects – accelerating the entire ML lifecycle.
HPE GreenLake ML Operations Platform
Moving ML models to production requires infrastructure monitoring, model versioning and automated retraining. GreenLake ML Ops provides the operational platform for production machine learning. It tracks model performance, detects model drift, triggers retraining automatically and manages compute resource allocation across multiple models. Eliminates the operational bottleneck that prevents many organisations from scaling beyond initial models.
HPE Pointnext Professional Services
HPE Pointnext combines AI architecture review, infrastructure optimisation and ML operations enablement. Services include strategy assessment to identify highest-impact use cases, infrastructure design to match workload requirements, and ongoing optimisation as AI maturity increases. Teams build AI capability within their organisation rather than depending on external consultants.
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AI infrastructure decisions have downstream consequences for software development practices and operational efficiency. Our specialists assess your current AI roadmap, identify where infrastructure is a bottleneck versus where process changes would deliver faster progress, and design infrastructure that scales as your AI maturity increases. We help you avoid building infrastructure for speculative use cases.
