Part 4/13:
Data Preparation: Gather and curate high-quality, representative training datasets.
Model Selection: Start with a suitable pre-trained language model.
Fine-Tuning Techniques: Apply various methods, including synthetic data generation, to adjust model parameters.
Validation and Deployment: Rigorously test the customized model before deploying it into production environments.
Crucially, the speaker notes that a successful fine-tuning process depends heavily on the quality and relevance of the data used.