Last week's post about democratizing AI engineering sparked some  interesting discussion on Hacker News. Also, some people didn’t like the AI images

From ClickOps to GitOps: The Evolution of AI App Development

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2024-11-21 12:30:06

Last week's post about democratizing AI engineering sparked some interesting discussion on Hacker News. Also, some people didn’t like the AI images on my other blog posts so for this I used a real photograph :P

Today, I want to dive deeper into a critical aspect of this transformation: the bridge between rapid prototyping and production-ready AI applications. This post is based on the latest conversation in the MLOps Community Podcast: Become an AI Engineer with Open Source.

When OpenAI launched GPTs, many of us (myself included) were skeptical. Yet another attempt at ChatGPT plugins, we thought. But something interesting started happening: businesses began using GPTs to solve real problems. At a recent conference, I met a film industry risk assessment team that had built a chain of GPTs to automate complex safety evaluations. They weren't AI engineers – they were domain experts who found a way to encode their knowledge into a useful AI tool.

The problem with tools like ChatGPT's GPTs is that they're trapped in a web interface. Remember Jenkins? The DevOps community collectively shuddered at configuration through click-ops. We learned that lesson: production systems need to be declarative, version-controlled, and reproducible.

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