AIM401ExpertBreakout sessionAI & Machine Learning Playbook 5 live updates

Beyond API Dependency: Fine-tuning Cost-Effective Models on AWS

What this session is about

As API costs for general-purpose LLMs rise, relying solely on off-the-shelf models can quickly undermine both cost control and system reliability. In this session, we share how Nearmap moved beyond API dependency by fine-tuning and distilling domain-specific models on AWS to analyze 300 million building permits for roof modifications. Well discuss our approach to generating and structuring training data, distilling large models into smaller, production-ready alternatives, evaluating trade-offs across model architectures, and making data-driven accuracy-versus-cost decisions before deployment. Attendees will leave with concrete patterns for shipping efficient, specialized models into production.

Playbook

Editorial commentary · what to actually do about this on Monday

The concept
Distillation + fine-tuning a smaller model on your domain can beat frontier-model APIs on narrow tasks at a fraction of the cost (often 50–100×).
Why it matters
Frontier model APIs are an OpEx tax that scales with usage. For specialised tasks (classification, extraction, narrow generation), you're paying for generality you don't use.
The hard parts
Generating high-quality training data is the actual challenge. Fine-tuning is the easy bit. Eval is hard at this scale — you need a held-out set the trained model never sees.
Playbook moves
(1) Use frontier models as labelers for the small model — they generate the training data. (2) Set explicit accuracy budgets, not just cost targets. (3) Plan for periodic refresh; data drifts.
The surprise
For genuinely domain-specific tasks, a fine-tuned 7B-class model often *beats* a frontier model on the metric that matters — because it overfits to *your* distribution. That's not a bug; it's the feature you're paying for. ---

Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.

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