This description is based on a conversation with 'Sam', which took place over several days in November 2024
AI model training is a multi-stage process that transforms raw data into intelligent, predictive systems. Each stage plays a crucial role in refining and optimizing data to ensure models perform efficiently and accurately. It all begins with pre-processing, where massive datasets are collected, cleaned, and structured for training. This is followed by post-preprocessing, a refinement stage that enhances data quality through feature selection, dimensionality reduction, and bias mitigation.
Once the data is prepared, the training phase begins, where machine learning models learn patterns and relationships from structured datasets. After training, models undergo fine-tuning and evaluation, ensuring accuracy and reducing errors.
Finally, the inference stage marks real-world deployment, where trained models generate predictions and responses based on new data inputs. Throughout this series, we explore each of these stages in depth, breaking down the key processes that power modern AI.