Generative AI and Data Mesh: Transforming Observability in the Edge-to-Cloud Era

Overview

Explore how Generative AI and Data Mesh architecture revolutionize enterprise observability, enabling advanced anomaly detection, automated root-cause analysis, and domain-focused insights across edge-to-cloud systems.

Syllabus

• Introduction to Generative AI and Data Mesh
Overview of Generative AI and its applications
Understanding Data Mesh architecture
• Enterprise Observability in the Edge-to-Cloud Era
Definition and importance of observability
Challenges in edge-to-cloud systems
• Generative AI for Advanced Anomaly Detection
Types of anomalies in data streams
Generative models for anomaly detection
Case studies and real-world applications
• Automated Root-Cause Analysis with Generative AI
Techniques for root-cause analysis
Role of AI in automating diagnostics
Implementing AI-driven investigation workflows
• Data Mesh Architecture in Observability
Key principles of Data Mesh
Decentralized data ownership and management
Data as a product in observability
• Domain-Focused Insights and Data Products
Creating data products for specific domains
Leveraging domain expertise in AI models
Enhancing observability through domain-driven insights
• Integrating Generative AI and Data Mesh
Synergies between Generative AI and Data Mesh
Designing a unified observability strategy
Best practices for integration
• Future Trends and Challenges
Emerging technologies in observability
Ethical and operational considerations
Preparing for future innovation and scalability
• Practical Sessions and Case Studies
Hands-on exercises with generative models
Developing a data mesh architecture for observability
Industry case studies and lessons learned
• Conclusion and Further Resources
Summary of key learnings
Recommended readings and tools
Opportunities for further study and research.

Subjects

Computer Science