ML Model Explainability
In the rapidly evolving world of machine learning (ML) and artificial intelligence (AI), developing models capable of delivering accurate predictions is no longer the sole objective. As organizations increasingly rely on data-driven decision-making, understanding the rationale behind a model’s predictions becomes paramount. The growing need for explainability in ML models stems from ethical, regulatory, and practical concerns, and it is here that the concept of Explainable AI (XAI) comes into play.
This chapter delves into the intricacies of Explainable ML models, a critical component in the MLOps landscape, with a focus on their implementation in the Google Cloud ecosystem. Although a comprehensive exploration of XAI techniques and tools is beyond this chapter’s scope, we aim to equip you with the knowledge and skills to build transparent, interpretable, and accountable ML models using the Explainable ML tools available on GCP.
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