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The case for embedding audit trails in AI systems before scaling
- Orchestration frameworks for AI services play a crucial role in managing workflows, agents, and audit trails for enterprises as they scale their AI services.
- Kevin Kiley, president of Airia, emphasizes the importance of auditability and traceability in AI systems to identify issues and ensure compliance.
- Building robustness and audit trails into AI systems early on can help prevent issues and ensure performance to standards before deployment.
- Many AI pilot programs initially lacked an orchestration layer or audit trail, posing challenges for traceability and monitoring AI performance.
- Organizations need to assess their data and establish dataset versioning to validate AI systems and make experiments reproducible.
- Transparency, openness, and alignment with compliance tools and responsible AI policies are crucial considerations for enterprises in building reliable AI pipelines.
- Open-source platforms like MLFlow, LangChain, and Grafana offer granular instructions and monitoring for AI models, while services from AWS and Microsoft help track AI tools' compliance.
- Enterprises must choose a transparent system that allows visibility into AI decision-making processes and offers flexibility for intervention when needed.
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