The essential knowledge every AI engineer needs. This 4-part series covers model customization approaches (fine-tuning, LoRA, prompting), production monitoring and observability, handling AI system failures gracefully, and the evaluation frameworks that catch problems before your users do.
Fine-tuning is powerful but often misused. Learn when to fine-tune, how to do it right (cloud and local), and why prompt engineering or RAG might be better choices.
Tom HundleyThe future isn't bigger models—it's smarter small ones. Learn how to distill large models into efficient, task-specific versions for production deployment.
Tom HundleyRLHF made ChatGPT useful. Understanding how reinforcement learning shapes AI behavior helps you understand what AI can—and can't—become in your organization.
Tom HundleyModels don't fail all at once—they drift. Learn to detect data drift, concept drift, and model drift before small degradations become major production failures.
Tom HundleyBegin with Part 1 and work your way through the series at your own pace.
Start with Part 1