To achieve business success with AI, you need rapid experimentation. Although the emerging world of machine learning operations (MLOps) offers many tools for iterative model training and deployment, most of them don’t streamline data management. And enterprise-caliber storage and data management platforms are often complex and unapproachable for the data scientists and data engineers who work on AI projects.
To fill this gap, we’ve developed the NetApp® Data Science Toolkit. This toolkit provides NetApp industry-leading, multitenant data management capabilities 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 data volumes in seconds. Because it also provides easy access to advanced storage features that would normally require help from a storage administrator, the toolkit delivers real business value by significantly speeding up projects.
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.