What Good AI Infrastructure Actually Looks Like

Most companies are building AI features on infrastructure that wasn't designed for AI. I'm seeing this across various industries right now.

The Five Pillars of `good` AI Infrastructure

Clean, versioned data pipelines:

Not a data lake, a data platform. Your model is only as good as what you feed it and as such invest in data quality before you invest in models.
Feature stores:

Centralised, reusable features shared across models will eliminate duplication whilst ensuring consistency between training and inference.
Scalable inference infrastructure:

Batch and real-time pipelines are fundamentally different. Design for both from the start, or you'll rebuild when it matters most.
Model versioning and rollback:

You need to deploy a model the same way you deploy code. You require the ability to roll back quickly if something goes wrong.
Drift monitoring:

Models degrade silently over time. Production data shifts. Without observability specifically built for ML, you won't know until your customers do.

The companies winning at AI built this foundation first. Then they added the models.

If you're planning an AI initiative and want to make sure your infrastructure can support it: https://lnkd.in/giMm28Hn