Skill

Model Tuning

Data Science, Analytics and AI/ML

The process of adjusting a machine learning model's hyperparameters (such as learning rate, batch size, or regularization strength) or fine-tuning a pretrained model's weights on new data to improve its performance on a target task. Data scientists and ML engineers use techniques like grid search, random search, or Bayesian optimization, and increasingly fine-tune large language models on domain-specific datasets to specialize their behavior. It is a key step for adapting general-purpose models to specific business needs.

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• Fine-tuning
• Transfer Learning
• Model Training
• Model Evaluation