Google Cloud published a blog post about "GenOps: learning from the world of microservices and traditional DevOps." The article discusses the need for a new ‘GenOps’ team to cater to the unique characteristics of generative AI applications, focusing on the similarities and differences with microservices and DevOps.
What I found particularly interesting was the analogy between an "AI agent" and a "microservice." Both are discrete, functional units, but an AI agent is distinguished by its non-deterministic behavior due to its reliance on AI models.
The article also provides insightful thoughts on model management and prompts, model evaluation, model security, and centralized tool management. Highlighting the importance of model reviews and approvals, prompt version management, continuous assessment of model response quality, a model security gateway, and centralized tool management is crucial to ensure responsible and effective deployment of generative AI applications.
I believe the concept of GenOps will become increasingly relevant as generative AI applications continue to evolve and mature. Organizations will need to adopt new practices and tools to ensure the success of Gen AI deployments.
