Module 5: Virtualized Unbiasing Framework
LESSON 5. 2: STAGES OF AI TRAININGS
The second section is a step-by-step presentation of bias in training a ML model. This process unfolds through several key stages. It begins with data collection, where relevant datasets are acquired to feed into the model. Following this, data preprocessing is of vital importance, focusing on the cleaning, normalization, and transformation of the raw data towards enhanced and effective learning. Feature selection follows, where meaningful attributes are chosen to enhance the model's performance and reduce complexity. Subsequently, the model training phase involves feeding the processed data into the chosen algorithm or architecture to enable it to learn patterns and relationships. The trained model is then evaluated using a separate validation set to gauge its accuracy and generalization capabilities. Once deemed satisfactory, the model proceeds to deployment, making it operational for real-world applications. Continuous monitoring and updates may follow to ensure its ongoing effectiveness, adapting to changes in data distribution or evolving problem domains. This cyclical process of data-driven learning, from collection to deployment, forms the foundation of ML model training.