As the adoption of AI in the enterprise continues to expand at a rapid pace, AI training workflows are becoming more complex. Data scientists and data engineers often need to pull data from multiple different data sources, and these data sources aren’t always compatible with each other. This presents a major challenge and causes many AI projects to underdeliver or even fail completely. It is now imperative that data scientists and data engineers have the tools necessary to construct unified data pipelines that incorporate different data sources, environments, platforms, and protocols. The latest version of the NetApp® Data Science Toolkit, version 1.1, enables data scientists and data engineers to directly trigger the movement of datasets—on demand or as a step in an automated workflow. Here's a rundown of what's new in version 1.1.
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.