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Starts 8 June 2025 12:40
Ends 8 June 2025
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Asimov's Zeroth Law of Robotics: Observability for AI
Explore the challenges of AI observability, from monitoring model drift to managing costs, with practical demos using OpenTelemetry, Prometheus, and OpenLit to ensure AI systems remain transparent and ethical.
CNCF [Cloud Native Computing Foundation]
via YouTube
CNCF [Cloud Native Computing Foundation]
2544 Courses
32 minutes
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Overview
Explore the challenges of AI observability, from monitoring model drift to managing costs, with practical demos using OpenTelemetry, Prometheus, and OpenLit to ensure AI systems remain transparent and ethical.
Syllabus
- Introduction to AI Observability
- Concepts in AI Observability
- Tools for AI Observability
- Practical Demos
- Managing Observability Costs
- Case Studies in Observability
- Challenges and Future Directions
- Conclusion
Definition and importance of AI observability
Overview of the Zeroth Law of Robotics and its relevance to AI ethics
Model drift detection and management
Ensuring transparency and ethical AI operations
Introduction to OpenTelemetry
Installation and configuration
Tracing, metrics, and logs in AI systems
Prometheus for AI monitoring
Setup and integration with AI systems
Query language and creating dashboards
OpenLit for ethical AI practices
Monitoring ethical AI guidelines
Case studies and examples of ethical breaches
Setting up observability using OpenTelemetry in a sample AI project
Monitoring an AI model for drift with Prometheus
Implementing ethical AI observability with OpenLit
Understanding the cost factors in AI observability
Efficient resource management strategies
Analysis of real-world scenarios and outcomes
Current challenges in AI observability
Future trends and innovations
Recap of key learnings
Final thoughts on maintaining transparent and ethical AI systems
Subjects
Computer Science
