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Duration 14 hours
Course Outline
Fundamentals of Responsible AI
- Core tenets of fairness, accountability, and transparency
- Key regulatory factors influencing responsible AI (e.g., EU AI Act, GDPR)
- How Ollama fits into enterprise AI governance strategies
Identifying and Reducing Bias
- Detecting bias within model generations
- Techniques for minimizing bias and enhancing equity
- Assessing model efficacy using fairness-based metrics
Secure Prompting and Model Alignment
- Crafting prompts for safety and consistency
- Addressing risks associated with harmful or unsafe responses
- Applying alignment methods for business-critical applications
Filtering and Moderation Strategies
- Structuring effective content filtering systems
- Introducing moderation controls and safeguards
- Striking a balance between user experience and regulatory requirements
Governance Process Design
- Establishing governance frameworks specific to Ollama
- Connecting workflows with existing compliance infrastructure
- Procedures for model approval and auditing
Logging, Traceability, and Audit Readiness
- Secure logging protocols for AI architectures
- Tracking the lineage of model decisions
- Mechanisms for audit preparedness and reporting
Case Studies and Industry Standards
- Enterprise implementations adhering to responsible AI guidelines
- Insights derived from previous governance shortcomings
- Culturing lasting, ethical AI operations
Recap and Future Directions
Requirements
- Foundational knowledge of AI/ML concepts
- Working understanding of compliance and governance frameworks
- Background in enterprise IT or model deployment contexts
Target Audience
- AI Ethics Specialists
- Compliance Professionals
- Legal and Regulatory Engineers
- Enterprise Architects
