Process-Based Supervision

Process-Based Supervision is a methodology in machine learning that emphasizes the importance of monitoring and guiding the training process of models. This approach focuses on the workflows and procedures involved in developing AI systems, ensuring that each step aligns with the desired outcomes. Key characteristics include continuous feedback loops, iterative improvements, and the integration of best practices throughout the model development lifecycle. Common use cases involve enhancing model performance, ensuring compliance with ethical standards, and adapting to changing data environments.