AWS Certified AI Practitioner (AIF-C01) certification study notes, this guide will help you with quick revision before the exam. it can use as study notes for your preparation.
Certain industries require a higher level of compliance due to strict regulatory and security requirements. These are known as regulated workloads and commonly include: Financial services, Healthcare, Aerospace
If we need to comply with regulatory frameworks (audit, archival, special security requirements), then we have a regulated workload
AI Standard Compliance Challenges
Complexity and Opacity: AI models, especially deep learning systems, often operate as “black boxes,” making it difficult to understand or audit how decisions are made.
Dynamism and Adaptability: AI systems are not static. They evolve over time as models are retrained or updated, which complicates ongoing compliance and validation.
Emergent Capabilities: AI systems may exhibit unintended or unexpected behaviors that were not explicitly designed or anticipated.
Unique Risks: algorithmic bias, privacy violations and misinformation
Algorithmic bias: Bias present in training data can be learned and amplified by the model
Human bias: Bias introduced by developers, data curators, or system designers
Privacy violations and misinformation
Algorithm Accountability: algorithms should be transparent and explainable
Regulations in the EU “Artificial Intelligence Act” and US (several state and cities)
Promote fairness, non-discrimination and human rights
AWS Compliance
AWS supports are over 140 security standards and compliance certifications
Examples:
National Institute of Standards and Technology (NIST)
European Union Agency for Cybersecurity (ENISA)
International Organization for Standardization (ISO)
AWS System and Organization Controls (SOC)
Health Insurance Portability and Accountability Act (HIPAA)
General Data Protection Regulation (GDPR)
Payment Card Industry Data Security Standard (PCI DSS)
These certifications help customers meet regulatory requirements when building AI solutions on AWS.
Model Cards
Standardized format for documenting the key details about an ML model
For GenAI models it can include source citations and data origin documentation
Details about the dataset used, their sources, licenses and any known biases or quality issues in the training data
Intended use, risk rating of a model, training details
SageMaker Model Cards: document your ML model in a centralized place