We’ve all been there. The project works great as a proof of concept, but when it comes time to move to production, progress stalls. Challenges around data management and traceability become painful roadblocks. Unfortunately, this is all-too-common a problem in the world of enterprise AI. Although the emerging machine learning operations (MLOps) ecosystem offers many tools for iterative AI model training and deployment, most of those tools don’t streamline data management. And those that do handle data management are often complex and force data scientists to manage storage resources separately from their data science workspaces.
To address this gap, we’ve developed the NetApp® Data Science Toolkit for Kubernetes, which is included in the newly released version 1.2 of the NetApp Data Science Toolkit. This toolkit abstracts storage resources and Kubernetes workloads up to the data science workspace level. Best of all, these capabilities are packaged in a simple, easy-to-use interface that’s designed for data scientists and data engineers. Using the familiar form of a Python program, the toolkit enables data scientists and engineers to provision and destroy JupyterLab workspaces in just seconds. These workspaces can contain terabytes, or even petabytes, of storage capacity, allowing data scientists to store all of their training datasets directly in their project workspaces. Gone are the days of separately managing workspaces and data volumes.
All of the under-the-hood storage and Kubernetes operations, which would otherwise require help from both a DevOps engineer and a storage administrator, are executed automatically. These self-service capabilities can significantly speed up AI projects, removing time-consuming IT request-response cycles.
Mike is a Technical Marketing Engineer at NetApp focused on MLOps and Data Pipeline solutions. He architects and validates full-stack AI/ML/DL data and experiment management solutions that span a hybrid cloud. Mike has a DevOps background and a strong knowledge of DevOps processes and tools. Prior to joining NetApp, Mike worked on a line of business application development team at a large global financial services company. Outside of work, Mike loves to travel. One of his passions is experiencing other places and cultures through their food.
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