This post was originally published on Info World
So, you’re building a cloud architecture and also designing generative AI-powered systems. What do you need to do differently? What do you need to do the same? What are the emerging best practices? After building a few of these in the past 20 years, and especially in the past two years, here are my recommendations:
Understand your use cases
Clearly define the purpose and goals of the generative AI within your cloud architecture. If I see any mistake repeatedly, it’s not understanding the meaning of generative AI within the business systems. Understand what you aim to achieve, whether it’s content generation, recommendation systems, or other applications.
This means writing stuff down and finding consensus on the objectives, how to address the goals, and most importantly, how to define success. This is not new with generative AI only; this is a step to win with every migration and net-new system built in the cloud.
I’m seeing whole generative AI projects in the cloud fail because they don’t have well-understood business use cases. Companies build something that is cool but does not return any value to the business. That won’t work.
Data sources and quality are key
Identify the data sources required for training and inference by the generative
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