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We can all be AI engineers - and we can do it with open source models

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We can all be AI engineers - and we can do it with open source models

The barriers to AI engineering are crumbling fast

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Luke Marsden
Nov 12, 2024
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We can all be AI engineers - and we can do it with open source models

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A couple of weeks ago, I gave a talk at Hannah Foxwell's amazing AI
for the Rest of Us conference about something that's been brewing in
my mind after years of working in DevOps, MLOps, and now GenAI: the
barriers to AI engineering are crumbling fast. The tools have gotten
good enough that if you can handle an IDE and push some YAML to git,
you're already qualified.

A Pattern We've Seen Before

Having lived through the evolution from DevOps (ClusterHQ) to MLOps
(Dotscience) and now diving deep into GenAI with HelixML, I keep
seeing the same pattern: complex tools get simpler, workflows get
standardized, and suddenly what seemed like rocket science becomes
just... more engineering.

Let's Break This Down

Building an AI application comes down to six building blocks:

 1. Models: Just mathematical functions. Complex ones, sure, but at
    their core, they're just turning words into numbers and back
    again.

 2. Prompts: Telling the model what to do in plain English. Sometimes
    you need to be really explicit - like talking to a toddler.

 3. Knowledge: Your AI's personal knowledge base. Documents,
    websites, whatever you need it to learn from.

 4. Integrations: This is where it gets interesting - connecting your
    AI to real business systems through APIs.

 5. Tests: Because nobody wants their AI going sideways in
    production. Yes, you can test AI apps, and you absolutely should.

 6. Deployment: Running it on a server. For example, versioning the
    entire app above in a yaml file and using Flux to manage
    deployment to K8s and Helix.

Taking It to Production

Here's where my DevOps background kicks in. You know all those tools
you're already using? Git? CI/CD pipelines? They work for AI apps
too. I demonstrated this live in the talk by building a Jira
integration that could write code based on ticket descriptions.

The secret sauce? Something we're calling an "AISpec" - a YAML file
that pulls all these pieces together in a way that feels natural to
anyone who's worked with modern infrastructure tools.

Why Open Source Models Matter

This bit's important: when you use open source models, your data
stays yours. It doesn't end up training someone else's model. For
companies worried about GDPR or other national, regional or corporate
regulation, this is crucial. You can run everything locally, in your
own infrastructure, with your data staying right where your legal
team wants it.

See It In Action

If you're reading this thinking "show me the code," I've got you
covered. Here's a complete reference architecture you can set up to
do this stuff yourself: https://github.com/helixml/genai-cicd-ref

And here's a complete tutorial of using and setting up the reference
architecture:

The main thing I want you to take away is this: if you can handle
version control and basic deployment workflows, you can build
production-ready AI applications. The tools are there. The models are
there. And they're getting better every day.

What's Next?

Check out aispec.org if you want to dig into the standard format
we're proposing for all this. Or if you want to chat about any of
this, find me on LinkedIn or drop me an email at luke@helix.ml.

This is about making AI engineering accessible to everyone who knows
how to ship code. No PhD required, no magic - just solid engineering
practices applied to these powerful new tools.

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