HPE Machine Learning Development Environment
Uncover hidden insights from your data by helping engineers and data scientists collaborate, build more accurate ML models and train them faster.
Accelerate ML model development
Learn about the comprehensive array of features integrated into the easy-to-use, high-performance solution of HPE Machine Learning Development Environment.
Train models faster
Allow ML engineers to train models faster and take advantage of distributed training without changing their model code. This gives teams the ability to train at any scale by managing the provisioning of machines, networking, data loading, and fault tolerance, making distributed model training fast and easy.
Remove complexity and cost
Enable ML model developers to accelerate time to value by making it easier for IT admins to set up, manage, secure, and share AI compute clusters. Developers get more from their GPUs with smart scheduling, as well as reduce cloud GPU costs by seamlessly using spot instances.
Enhance data science collaboration
Enable easier and faster ML team collaboration through features like simpler model reproducibility and experiment tracking. The result is that teams are able to easily interpret experiment results and reproduce experiments.
Build models, not infrastructure
Learn how ML engineers can focus on building better models, instead of managing IT infrastructure.
Take the next steps
Ready to get started? Explore purchasing options or engage with HPE experts to determine the best solution for your business needs.
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Technical specifications
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Train models faster
Allows ML engineers to train models faster, allowing them to take advantage of distributed training without changing their model code.
-
Remove complexity and cost
Enables ML model developers to accelerate time to value by making it easier for IT admins to set up, manage, secure, and share AI compute clusters.
-
Enhance data science collaboration
Enables easier and faster ML team collaboration through features like simpler model reproducibility and experiment tracking.
-
Flexible AI infrastructure support
Offers companies broad infrastructure flexibility, allowing it to run on a variety of compute environments such as cloud and on-premises AI infrastructure.