TR-4798: NetApp AI Control Plane

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Mike Oglesby, NetApp

Companies and organizations of all sizes and across many industries are turning to artificial intelligence (AI), machine learning (ML), and deep learning (DL) to solve real-world problems, deliver innovative products and services, and to get an edge in an increasingly competitive marketplace. As organizations increase their use of AI, ML, and DL, they face many challenges, including workload scalability and data availability. This document demonstrates how you can address these challenges by using the NetApp AI Control Plane, NetApp’s full stack AI data and experiment management solution.

This report shows you how to rapidly clone a data namespace just as you would a Git repo. It also shows you how to seamlessly replicate data across sites and regions in order to create a cohesive and unified AI/ML/DL data pipeline. Additionally, it walks you through the defining and implementing of AI, ML, and DL training workflows that incorporate the near-instant creation of data and model baselines for traceability and versioning. With this solution, you can trace every model training run back to the exact dataset that was used to train and/or validate the model. Lastly, this document shows you how to swiftly provision Jupyter Notebook workspaces with access to massive datasets.

Because this solution is targeted towards data scientists and data engineers, no NetApp or NetApp ONTAP expertise is required. With this solution, data management functions can be executed using simple and familiar tools and interfaces. Furthermore, this solution uses fully open source and free components. Therefore, if you already have NetApp storage in your environment, you can implement this solution today. If you want to test drive this solution but you do not have already have NetApp storage, visit, and you can be up and running with a cloud-based NetApp storage solution in minutes. The following figure provides a visualization of the solution.

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