Summary
This chapter delved deeply into various AI risk topics and techniques, including bias, explainability, privacy, and adversarial attacks. Additionally, you should be familiar with some of the technology capabilities offered by AWS to facilitate model risk management processes, such as detecting bias and model drift. Through the lab section, you gained hands-on experience with utilizing SageMaker to implement bias detection, model explainability, and privacy-preserving model training.
In the next chapter, we will shift our focus to the ML adoption journey and how organizations should think about charting a path to achieve ML maturity.
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