Level 04 · Pro / Advanced

Deploying AI at Scale

You're shipping AI in production, or making decisions about it at an organisational level. This track is about the judgment calls that don't show up in a tutorial.

Last updated: September 2026

What this track covers

Evaluation. How to actually measure whether an AI system is good enough to ship, beyond "it looked fine in testing."
Cost and latency trade-offs. Choosing between model sizes, providers, and self-hosting based on real unit economics, not benchmark leaderboards.
Risk and governance. Data privacy, model drift, and building review processes for AI output before it reaches customers.
Staying current without drowning. A sustainable way to track a field that changes every few weeks, without chasing every announcement.

A common mistake at this level: optimising for the newest, most capable model instead of the most reliable and cost-effective one for the specific task. Bigger isn't always better once you're paying per call at scale.

Go deeper with these lessons

The questions I would ask before scaling

Production AI is less about finding the “best model” and more about controlling a system whose output is probabilistic. Before increasing traffic or automating a decision, I would want clear answers to the questions below. They force the conversation away from demos and toward operational reality.

Pre-deployment review

Value: what measurable task improves, and what is the non-AI baseline?
Evaluation: which test set and failure threshold decide whether a release is acceptable?
Human control: which outputs can be automated, and which require review before they affect a customer or business decision?
Data: what information is sent to the model, where is it retained, and who is allowed to access it?
Economics: what does one completed task cost at expected volume, including retries and surrounding infrastructure?
Fallback: what happens when the provider is unavailable, latency spikes, or output fails validation?

Advanced checkpoint

A mature deployment should have an owner, an evaluation set, a rollback or fallback path, documented data handling, and a reason for using AI that survives a cost comparison with a simpler solution.