What's changed: Created Professional Machine Learning Engineer Chapter 6 (Domain 6 "Monitoring": secure AI systems (theft/poisoning defense), Google Responsible AI (bias monitoring), fairness/readiness assessment, Vertex Explainable AI (feature attributions); Vertex AI Model Monitoring continuous evaluation, training/serving skew, feature attribution drift, performance vs baselines/simpler models/over time, common training/serving error monitoring).
6.1AI risks and Responsible AI
Understand building secure AI systems by protecting against unintentional exploitation of data/models (hacking), aligning with Google's Responsible AI practices (monitoring for bias), assessing AI solution readiness (fairness, bias), and model explainability on Vertex AI (Explainable AI).
AI brings risks alongside benefits. Build trustworthy AI by designing in security, fairness, and explainability.
6.1.1Secure AI and Responsible AI
Protect AI systems from unintentional exploitation (attacks) on training data/models—guard against theft/poisoning (data poisoning) and adversarial inputs with least access, VPC Service Controls, CMEK, and input validation. Align with Google's Responsible AI practices: build in bias monitoring, fairness, and suppression of harmful outputs. Before release, assess AI solution readiness for fairness, bias, and safety. Map "protect training data/models = least access + perimeter + encryption" and "unbiased AI = bias monitoring and fairness evaluation."
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