Platform and Model Design for Responsible AI

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ML Pipeline, Model Evaluation, and Handling Uncertainty

This chapter starts with the introduction of the AI/ML workflow. The chapter then delves into different ML algorithms used for classification, regression, generation, and reinforcement learning. The chapter also discusses issues related to the reliability and trustworthiness of these algorithms. We start with an introduction to the various components of an ML pipeline and explain the need for continuous training. The chapter then briefly explores the important AI/ML algorithms for the tasks of classification, regression, and clustering. Further, we discuss approaches for identifying bias in learning algorithms and causes of uncertainty in model prediction.

In this chapter, these topics ...