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HPE Machine Learning Ops 1yr Select Subscription 24x7 Support E-LTU

HPE Machine Learning Ops 1yr Select Subscription 24x7 Support E-LTU

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HPE Machine Learning Ops 1yr Select Subscription 9x5 Support E-LTU

HPE Machine Learning Ops 1yr Select Subscription 9x5 Support E-LTU

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HPE Machine Learning Ops 1yr Universal Subscription 24x7 Support E-LTU

HPE Machine Learning Ops 1yr Universal Subscription 24x7 Support E-LTU

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HPE Machine Learning Ops 1yr Universal Subscription 9x5 Support E-LTU

HPE Machine Learning Ops 1yr Universal Subscription 9x5 Support E-LTU

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HPE Machine Learning Ops 2yr Select Subscription 24x7 Support E-LTU

HPE Machine Learning Ops 2yr Select Subscription 24x7 Support E-LTU

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HPE Machine Learning Ops 2yr Select Subscription 9x5 Support E-LTU

HPE Machine Learning Ops 2yr Select Subscription 9x5 Support E-LTU

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HPE Machine Learning Ops 2yr Universal Subscription 24x7 Support E-LTU

HPE Machine Learning Ops 2yr Universal Subscription 24x7 Support E-LTU

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HPE Machine Learning Ops 2yr Universal Subscription 9x5 Support E-LTU

HPE Machine Learning Ops 2yr Universal Subscription 9x5 Support E-LTU

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HPE Machine Learning Ops 3yr Select Subscription 24x7 Support E-LTU

HPE Machine Learning Ops 3yr Select Subscription 24x7 Support E-LTU

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HPE Machine Learning Ops 3yr Select Subscription 9x5 Support E-LTU

HPE Machine Learning Ops 3yr Select Subscription 9x5 Support E-LTU

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Nouveautés

  • Leverage the power of containers to create complex machine learning and deep learning stacks including TensorFlow, Apache Spark on Yarn with Kerberos, H2O, and Python ML and DL toolkits.
  • Spin-up distributed, scalable, ML, and DL training environments in minutes rather than months—on-premises, public cloud, or in a hybrid model.
  • Use your choice of tools to support even the most complex ML flow. For example, start with data prep in Spark, follow with training in TensorFlow on GPUs, and deploy on CPUs with TensorFlow runtime.
  • Implement CI/CD processes for your ML projects with a model registry. Model registry stores models and versions created within HPE ML Ops as well as those created using different tools/platforms.
  • Improve the reliability and reproducibility of ML projects on a shared project repository (GitHub).
  • Deploy models in production with reliable, scalable, and highly available endpoint deployment with out-of-the-box autoscaling, and load balancing.

Caractéristiques clés

Faster Time to Value

Manage and provision infrastructure through an intuitive graphical user interface.

Provision development, test, or production environments in minutes as opposed to days.

Onboard new data scientists rapidly with their choice of tools and languages without creating siloed development environments.

Improved Productivity

Data scientists spend their time building models and analyzing results rather than waiting for training jobs to complete.

BlueData,recently acquired by Hewlett Packard Enterprise, helps ensure no loss of accuracy or performance degradation in multi-tenant environments.

Increase collaboration and reproducibility with shared code, project, and model repositories.

Reduced Risk

Enterprise-grade security and access controls on compute and data.

Lineage tracking provides model governance and auditability for regulatory compliance.

Integrations with third-party software provides interpretability.

High-availability deployments help ensure critical applications do not fail.

Flexible and Elastic

Deploy on-premises, cloud, or in a hybrid model to suit your business requirements.

Autoscaling of clusters to meet the requirements of dynamic workloads.

* Les prix peuvent varier selon le revendeur local.

Comment pouvons-nous vous aider ?

Profitez de conseils, de réponses et de solutions lorsque vous en avez besoin. Pour toute question générale,envoyez-nous un e-mail à hpestore.quote-request@hpe.com

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