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Duration 14 hours
Course Outline
Foundations of Responsible AI
- Core tenets of fairness, accountability, and transparency
- Regulatory catalysts driving responsible AI adoption (e.g., EU AI Act, GDPR)
- Ollama's strategic role in enterprise AI governance
Identifying and Mitigating Bias
- Detecting bias within model outputs
- Tactical approaches to bias reduction and enhancing fairness
- Assessing model performance using fairness-specific metrics
Safe Prompting and Alignment
- Engineering prompts for maximum safety and reliability
- Managing risks associated with unsafe or harmful model responses
- Applying alignment techniques suited for enterprise-grade applications
Content Filtering and Moderation
- Constructing efficient content filtering pipelines
- Deploying robust moderation safeguards
- Navigating the balance between user experience and compliance obligations
Governance Workflows
- Defining tailored governance frameworks for Ollama
- Integrating workflows with existing compliance systems
- Streamlining model approval processes and audit protocols
Logging, Traceability, and Auditability
- Implementing secure logging protocols for AI systems
- Ensuring full traceability of model-driven decisions
- Maintaining audit readiness and effective reporting mechanisms
Case Studies and Industry Best Practices
- Enterprise deployments prioritising responsible AI principles
- Insights gained from real-world governance challenges
- Cultivating sustainable and ethical AI operational practices
Recap and Future Directions
Requirements
- Solid grasp of AI/ML fundamentals
- Knowledge of compliance and governance frameworks
- Hands-on experience in enterprise IT or model deployment environments
Target Audience
- AI ethics specialists
- Compliance professionals
- Legal and regulatory engineers
- Enterprise architects
