Model Training
Model training is the process of teaching an AI system to recognize patterns, make predictions, or generate outputs by exposing it to labeled or unlabeled data and adjusting its parameters.
In Depth
Model training is the fundamental process that creates AI capabilities. During training, a model processes thousands to billions of examples, adjusting millions or billions of internal parameters to minimize prediction errors. For customer support AI, training data typically includes historical conversations, resolved tickets, knowledge base articles, and product documentation.
The training process involves data preparation (cleaning, labeling, and formatting), architecture selection (choosing the right model type), hyperparameter tuning (optimizing learning rate, batch size, etc.), validation (testing on held-out data), and deployment. Continuous training — regularly updating models with new data — ensures AI agents stay current with product changes, new support scenarios, and evolving customer language patterns.
Related Terms
Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on domain-specific data to specialize its behavior and improve performance for particular tasks.
Machine Learning
Machine learning is a branch of AI where systems learn patterns from data and improve their performance over time without being explicitly programmed for each task.
Deep Learning
Deep learning is a subset of machine learning that uses multi-layered neural networks to learn complex patterns and representations from large volumes of data.
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