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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory requirements driving responsible AI adoption (e.g., EU AI Act, GDPR)
- The role of Ollama in enterprise AI governance
Identifying and Mitigating Bias
- Recognizing bias in model outputs
- Techniques for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Designing prompts for safety and reliability
- Strategies to mitigate risks from unsafe or harmful outputs
- Alignment techniques suited for enterprise applications
Content Filtering and Moderation
- Building content filtering pipelines
- Implementing effective moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks specifically for Ollama
- Integrating workflows with existing compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditability
- Best practices for secure logging in AI systems
- Ensuring traceability of model decisions
- Maintaining audit readiness and implementing reporting mechanisms
Case Studies and Best Practices
- Enterprise implementations adhering to responsible AI principles
- Key takeaways from real-world governance challenges
- Developing sustainable and ethical AI practices
Conclusion and Next Steps
Requirements
- A solid understanding of AI/ML fundamentals
- Knowledge of compliance and governance concepts
- Experience working with enterprise IT or model deployment environments
Target Audience
- AI ethics leaders
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
