What is Explainable AI and why is it important for MLOps practitioners?
XAI refers to methods and techniques that are used in the domain of AI that aim to make the decision-making processes of AI models transparent, interpretable, and understandable to humans. Instead of acting as black boxes where input data goes in and a decision or prediction comes out without clarity on how the decision was reached, XAI seeks to provide insights into the inner workings of models. This transparency allows users, developers, and stakeholders to trust and validate the system’s decisions, ensuring they align with ethical, legal, and practical considerations.
As ML continues to advance and its applications permeate various industries, the need for transparent and interpretable models has become a pressing concern. XAI aims to address this by developing techniques for understanding, interpreting, and explaining ML models. For MLOps practitioners working with Google Cloud, incorporating XAI...
