Google Cloud published a blog post on "GenOps: the evolution of MLOps for gen AI." This blog post focuses on how GenOps, or MLOps for Generative AI, addresses the operational challenges organizations face as they move to deploy Generative AI solutions at scale. I found this topic to be incredibly timely, given the rapid advancements we're seeing in the field of Generative AI.
The blog post does a great job of highlighting the unique challenges that Gen AI models present to traditional MLOps practices, such as the need for scale, compute, safety, rapid evolution, and unpredictability.
I particularly appreciated how the post breaks down the key capabilities of GenOps, including Gen AI experimentation and prototyping, prompt management, optimization, safety, fine-tuning, version control, deployment, monitoring, and security and governance.
The article also provides a clear explanation of how to extend the MLOps pipeline to support GenOps, with a focus on Google Cloud.
Overall, I found this blog post to be a valuable resource for anyone looking to understand the operational considerations of deploying Gen AI models at scale. It provides a comprehensive overview of the key challenges and considerations, as well as practical guidance on how to address them using Google Cloud.
