Five Steps to Build a New AI Model

Summary

The article outlines a five-stage workflow for developing AI models using foundation models, emphasizing data preparation, model training, validation, fine-tuning, and deployment.

Why it matters

Foundation models simplify the creation of specialized AI by fine-tuning them for specific domains, reducing time and computational costs compared to building models from scratch. This workflow also integrates governance through data and model cards, addressing ethical and compliance concerns.

Highlights

• Stage 1: Collect massive-scale data while filtering for language, profanity, copyright, and duplicates to build a governed foundational dataset.
• Stage 2: Choose the appropriate foundation model type (generative, encoder-only, etc.) and tokenize trillions of tokens for training.
• Stage 4: Empower not only data scientists but also application developers to quickly fine-tune models with prompts and local data.

Critical take

The workflow assumes access to petabytes of diverse data and thousands of GPUs, which may be unrealistic for small organizations lacking large cloud resources.