Solving the unpredictability of AI with SLA-driven Storage-as-a-Service

Nearly every organization recognizes the transformative power of AI to solve real world problems and deliver competitive advantages for their organization. But the exponential growth of AI applications and use cases, coupled with the proliferation of GPU-accelerated computing, makes sizing the storage environment to support these data-hungry workloads difficult to achieve. Deploying too little storage creates performance bottlenecks for your expensive compute, data science, and AI development investments, while over-provisioning leads to cost inefficiencies. Compounding these challenges, many AI projects start small and grow over time. This makes storage sizing even more difficult to forecast and guessing at the beginning of a 3-5 year refresh cycle isn’t going to cut it. Leveraging the cloud is an option, but as AI projects grow, costs become prohibitive. Additionally, much of the critical data needed to train differentiated AI models often resides in on-premises storage environments where data privacy, security, compliance and performance requirements necessitate keeping it under your control.