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Hi StreamSets Community! I’m Drew Nicolette, a data engineer in the public sector. I have a passion for teaching, learning and contributing to data engineering communities. I’ve recently published my first blog through the StreamSets Write for Us program, “MLOps: How Data Teams Can Give ML Algorithms Life and Longevity” and I’d love everyone’s thoughts. Feel free to leave any comments to open up a conversation.

 

In this post, I discuss the three steps of the Machine Learning model workflow, MLOps, and the four main reasons I see the need of and value in MLOps. As I look into the future, I see MLOps becoming the de facto way enterprise ML is conducted, so if you’re not already, let’s get familiar with it and its value. I only scratch the surface in this post, so I’d love to keep the discussion going here!

 

Please feel free to share the post on your own channels as well. 

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