Part 2/11:
This cycle is familiar across most industries. However, when it comes to AI, this traditional process doesn't hold because AI development involves distinctive phases centered around data, experimentation, and model deployment.
The Unique AI Lifecycle
Unlike conventional apps, AI development revolves around an end-to-end process including:
Data collection and preprocessing
Model building and experimentation
Fine-tuning and training
Deployment for inference
The speaker emphasized that all AI projects start with data, emphasizing the importance of gathering clean, normalized, and well-framed datasets. From there, models are built, trained, fine-tuned, and finally deployed into inference systems.