HN812: Nokia EDA: AI Ops You Can Trust (Sponsored)

Nokia's Steven Butler joins Heavy Networking to discuss how their event-driven automation (EDA) platform supports AI Ops by focusing on multi-vendor abstractions, consistent data models, extensibility, and digital twin capabilities. A key element of EDA is its abstraction layer, which normalizes configuration and state across different network operating systems, providing AI tools with the necessary context to understand network health. The discussion highlights the importance of well-defined and extensible schemas for AI to effectively interpret data and utilize various network tools. EDA's architecture allows users to create custom apps and workflows, enhancing AI's ability to troubleshoot and manage networks, and the platform's digital twin feature enables validation of AI-driven configuration changes before deployment.

Outlines

Part 1: Introduction, Multi-Vendor Strategy

Part 2: Data Models, Schemas, AI Integration

Part 3: Architecture, Modularity, Extensibility

Part 4: Ecosystem, Digital Twins, Conclusion

Sign in to continue reading, translating and more.

Continue

Heavy Networking

Part 1: Introduction, Multi-Vendor Strategy

Network Automation Platforms and AI Ops: An Introduction

EDA's Multi-Vendor Approach: Abstraction and OS Support

Multi-Vendor Abstractions: Configuration, State, and Normalization

Part 2: Data Models, Schemas, AI Integration

EDA as an Operations and Observability Tool for AI

Data Model Schemas: Supporting Operational Workflows and LLMs

Industry Standards and Extensibility of EDA's Data Model

Integration with Network Sources of Truth and Troubleshooting with AI

Part 3: Architecture, Modularity, Extensibility

Repeatable Tools and Deterministic AI for Network Operations

Extensibility of Models and Schemas in EDA for AI Ops

EDA's Architecture: Apps, Workflows, and Modularity

Customization and Abstraction in EDA for Network Automation

Part 4: Ecosystem, Digital Twins, Conclusion

Community Ecosystem and Sharing of Apps in EDA

Platform Extensibility and AI Ops: A Practical Example

Digital Twin Strategy: Containerized Environments and Pre-Validation

AI and Digital Twins: Validating Changes and Guardrails

EDA Test Drive, Contact Information, and Podcast Conclusion

HN812: Nokia EDA: AI Ops You Can Trust (Sponsored)

Heavy Networking

Part 1: Introduction, Multi-Vendor Strategy

00:00Network Automation Platforms and AI Ops: An Introduction

Network Automation Platforms and AI Ops: An Introduction

01:13EDA's Multi-Vendor Approach: Abstraction and OS Support

EDA's Multi-Vendor Approach: Abstraction and OS Support

03:41Multi-Vendor Abstractions: Configuration, State, and Normalization

Multi-Vendor Abstractions: Configuration, State, and Normalization

Part 2: Data Models, Schemas, AI Integration

07:21EDA as an Operations and Observability Tool for AI

EDA as an Operations and Observability Tool for AI

11:42Data Model Schemas: Supporting Operational Workflows and LLMs

Data Model Schemas: Supporting Operational Workflows and LLMs

14:11Industry Standards and Extensibility of EDA's Data Model

Industry Standards and Extensibility of EDA's Data Model

17:04Integration with Network Sources of Truth and Troubleshooting with AI

Integration with Network Sources of Truth and Troubleshooting with AI

Part 3: Architecture, Modularity, Extensibility

21:16Repeatable Tools and Deterministic AI for Network Operations

Repeatable Tools and Deterministic AI for Network Operations

25:07Extensibility of Models and Schemas in EDA for AI Ops

Extensibility of Models and Schemas in EDA for AI Ops

27:22EDA's Architecture: Apps, Workflows, and Modularity

EDA's Architecture: Apps, Workflows, and Modularity

30:23Customization and Abstraction in EDA for Network Automation

Customization and Abstraction in EDA for Network Automation

Part 4: Ecosystem, Digital Twins, Conclusion

34:22Community Ecosystem and Sharing of Apps in EDA

Community Ecosystem and Sharing of Apps in EDA

36:40Platform Extensibility and AI Ops: A Practical Example

Platform Extensibility and AI Ops: A Practical Example

39:38Digital Twin Strategy: Containerized Environments and Pre-Validation

Digital Twin Strategy: Containerized Environments and Pre-Validation

42:32AI and Digital Twins: Validating Changes and Guardrails

AI and Digital Twins: Validating Changes and Guardrails

47:09EDA Test Drive, Contact Information, and Podcast Conclusion

EDA Test Drive, Contact Information, and Podcast Conclusion