Solve Your AI Challenges with These AI Open-Source Tools

Mike McNamara

2021-03-09

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As organizations increase their use and spending ($110.7 billion by 2024) on artificial intelligence (AI) and machine learning (ML), they face challenges in data management, deployment complexity, and data availability. Many frameworks and toolkits in the industry attempt to make data more scalable and easier to deploy, but most fail to address the crucial challenge of data management and data availability. Many also feature proprietary data platforms that lack proven, enterprise-class reliability.

The NetApp^® AI Control Plane and Data Science Toolkit address these challenges. They simplify data management, streamline AI workflows, and help you get the most out of your data.

AI Control Plane and Data Science Toolkit

The AI Control Plane is a full-stack solution for managing AI data and experimentation; it integrates Kubernetes and Kubeflow with a data fabric enabled by NetApp. Kubernetes, the industry-standard container orchestration platform for cloud-native deployments, makes workloads more scalable and portable. Kubeflow is an open-source ML platform that simplifies management and deployment. And when your data fabric is powered by NetApp, you get uncompromising data availability and portability so that your data is accessible across the pipeline, from edge to core to cloud.

The NetApp Data Science Toolkit is a Python library that makes it easy for data scientists and data engineers to perform numerous data management tasks. These tasks include provisioning a new data volume, cloning a data volume almost instantaneously, and creating a NetApp Snapshot™ copy of a data volume for traceability and baselining. Traceability can add hours to AI operations—hours that the data scientist spends waiting instead of experimenting. The Data Science Toolkit reduces those hours to seconds.

The Data Science Toolkit also enhances the NetApp AI Control Plane by making it much easier to manage data. For example, a data scientist working on a Jupyter Notebook that was provisioned using the AI Control Plane can use the toolkit to implement a data management task in one simple line of Python code. The toolkit can also integrate advanced NetApp data management capabilities into other MLOps platforms—including custom and homegrown platforms—or serve as a standalone solution for teams that don’t need the overhead of a full-blown MLOps platform.

Watch these short videos to see how you can provision a new data volume in minutes and almost instantaneously create an exact copy of a data volume—all using the Data Science Toolkit.

Provision a new data volume

Near-instantaneously clone a data volume

The AI Control Plane and Data Science Toolkit are compatible with NetApp Cloud Volumes ONTAP^®software, so teams can use on-demand cloud compute resources in AWS, Microsoft Azure, or Google Cloud. To learn more, visit our NetApp AI page.

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。

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