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Duration 14 hours
Course Outline
Introduction to Responsible AI
- Core principles of fairness, accountability, and transparency
- Regulatory influences on responsible AI (including the EU AI Act and GDPR)
- Ollama’s specific role in enterprise AI governance
Detecting and Mitigating Bias
- Recognizing bias in model outputs
- Approaches for reducing bias and enhancing fairness
- Assessing model performance using fairness metrics
Safe Prompting and Alignment
- Crafting prompts for safety and reliability
- Reducing the risk of unsafe or harmful outputs
- Applying alignment techniques for enterprise use cases
Content Filtering and Moderation
- Architecting content filtering pipelines
- Implementing moderation safeguards
- Striking a balance between user experience and compliance requirements
Governance Workflows
- Establishing governance frameworks specific to Ollama
- Integrating workflows with compliance systems
- Procedures for model approval and auditing
Logging, Traceability, and Auditability
- Best practices for secure logging in AI systems
- Tracking the traceability of model decisions
- Maintaining audit readiness and reporting mechanisms
Case Studies and Best Practices
- Enterprise deployments that prioritize responsible AI
- Insights gained from real-world governance challenges
- Developing sustainable and ethical AI practices
Summary and Next Steps
Requirements
- A solid grasp of AI and machine learning fundamentals
- Knowledge of compliance and governance principles
- Background in enterprise IT or model deployment environments
Target Audience
- Leads in AI ethics
- Compliance officers
- Legal and regulatory engineers
- Enterprise architects
