| | Three Benefits for Separating Compute and Storage for Today's AI and Analytics Workloads | | | | | | Hi David,
Today's AI/ML and analytics workloads can be bursty and unpredictable. Provisioning infrastructure for worst-case (maximum load) compute scenarios is costly and increases administrative overhead. Kubernetes solves for this, partially, by letting organizations orchestrate and spin up containers as compute needs arise. Yet challenges persist. In this webinar, you will learn how new innovations from MapR simplify the independent scaling of compute and storage. You will discover how these innovations will help your organization: - Better handle compute needs that are bursty and unpredictable.
- Run compute in a truly multi-tenant environment, ensuring tenants do not starve each other of precious resources such as CPU and memory.
- Easily deploy and scale compute across different types of environments, including on-premises, public, and hybrid cloud.
- More effectively manage use cases (e.g., next-gen applications and data lake) in an increasingly hybrid cloud environment.
Regards,
MapR Team
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