Training: The initial phase where the model learns from a large dataset. This involves feeding the model vast amounts of text data and using techniques like supervised or unsupervised learning to adjust the model's parameters.
[: "Training is the foundational step where the AI model learns from data." (DeepMind, 2018), Customization: This involves fine-tuning the pretrained model on specific datasets related to the intended application. Customization makes the model more accurate and relevant for particular tasks or industries., Reference: "Customization tailors the AI model to specific tasks or datasets." (IBM Research, 2021), Inferencing: The deployment phase where the trained and customized model is used to make predictions or generate outputs based on new inputs. This step is critical for real-time applications and user interactions., Reference: "Inferencing is where AI models are applied to new data to generate insights." (Google AI, 2019), , ]
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