Speed up your data science initiatives

Mike McNamara
Mike McNamara

NetApp and Run:AI have partnered to simplify the orchestration of AI workloads, streamlining the process of both data pipelines & machine scheduling for DL.Fast experimentation and successful business outcomes of AI are directly correlated, but many AI projects are rife with inefficient processes. The combination of data processing time and outdated storage solutions creates bottlenecks and workload orchestration issues, and static allocation of GPU compute resources limits the number of experiments that researchers can run.

NetApp and Run:AI have partnered to simplify the orchestration of AI workloads, streamlining the process of both data pipelines and machine scheduling for deep learning (DL). With the NetApp® ONTAP® AI proven architecture, you can fully realize the promise of AI and DL by simplifying, accelerating, and integrating your data pipeline. And to help your researchers manage and optimize GPU utilization, Run:AI’s orchestration of AI workloads adds a Kubernetes-based scheduling and resource utilization platform.

Together, NetApp and Run:AI products enable numerous experiments to run in parallel on different compute nodes, with fast access to many datasets on centralized storage. With the combined solution from NetApp, NVIDIA, and Run:AI, you get an infrastructure stack that is purpose-built for enterprise AI workloads.Infrastructure stack for AIBy using Run:AI’s centralized resource pooling, queueing, and prioritization mechanisms together with NetApp ONTAP AI, your researchers are removed from infrastructure management hassles and can focus exclusively on data science. You can increase productivity by running as many workloads as you need without bottlenecks in your compute or data pipeline. And with the Run:AI scheduler and virtualization technology, you can easily use fractional GPUs, integer GPUs, and multiple nodes of GPUs for distributed training on Kubernetes. In that way, AI workloads run based on need, not on capacity. Your data science teams can run more AI experiments on the same infrastructure.

Run:AI DashboardWith NetApp and Run:AI technology, if your company scales AI, you get a double benefit: faster experiments and full resource utilization. To learn how to streamline and accelerate your data science initiative, read the technical report.

Mike McNamara

Mike McNamara

Mike McNamara 是 NetApp 產品和解決方案行銷的資深主管，在資料管理和雲端儲存行銷領域擁有超過 25 年的豐富經驗。在十年前加入 NetApp 之前，Mike 曾任職於 Adaptec、Dell EMC 和 HPE 等公司。Mike 是推出第一方雲端儲存產品和業界第一款雲端連線 AI/ML 解決方案 (NetApp)、統一化橫向擴充和混合雲儲存系統與軟體 (NetApp)、iSCSI 和 SAS 儲存系統與軟體 (Adaptec)，以及光纖通道儲存系統 (EMC CLARiiON) 的重要團隊領導者。此外他曾經擔任「光纖通道產業協會 (Fibre Channel Industry Association，FCIA)」的行銷主席，也是乙太網路技術高峰會議顧問委員會、乙太網路聯盟的成員，現在仍定期為業界期刊撰稿，並經常擔任活動講師。Mike 還透過 FriesenPress 出版了一本名為《橫向擴充儲存設備 - 企業資料管理的未來樣貌》的書籍，並被 Kapos 列為值得關注的 50 名 B2B 產品行銷人員。查看 Mike McNamara 的所有文章

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Speed Up Your Data Science Initiatives | NetApp Blog