Easily run big data and AI/ML workloads on Kubernetes

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Big data and AI/ML

Enable business intelligence at enterprise scale

Enterprises from manufacturing, financial services, utilities, and many other industries are implementing big data and AI/ML strategies to generate business intelligence and drive value across the organization, especially in areas like process automation, improving customer experience, cost reduction and revenue growth. However, it can be a challenge to run complex, I/O-intensive workloads on infrastructure with “noisy neighbors” — while maximizing compute and storage resource utilization. 

Diamanti makes it easy to deploy big data and AI/ML workloads on Kubernetes, effortlessly optimize CPU, storage and networking resources while supercharging performance and lowering the cost of your large-scale data investments.

Lightning performance with simplified deployment, storage and networking

Diamanti provides a true end-to-end platform to accelerate big data and AI/ML use cases. With GPU support for high-performance workloads, I/O acceleration and Kubeflow integration for machine learning workloads in Kubernetes, Diamanti makes it easy to supercharge your performance and optimize costs — whether on-premises or in the cloud. Moreover, with simplified management for GPU and non-GPU-based resources, data scientists have the flexibility they need to accelerate model training and deploy CPU-optimized workloads to the same cluster.

Proven results for large-scale containerized workloads

Supercharge any cloud native big data or AI/ML project with guaranteed Diamanti QoS. Leveraging Diamanti for large-scale workloads, enterprises are able to:

  • Deploy GPU and CPU-targeted workloads to the same cluster
  • Maximize resource utilization and return on investment
  • Experiment, train and run production ML models on a single platform
  • Reduce footprint by >50%