AI 

AI and Automation Workshop

Transcript

https://otter.ai/u/YmJhQ_votz-1KFWzpSphTl4CxD4?view=summary

The meeting discussed the integration of AI with automation platforms, particularly focusing on Red Hat AI. Key points included the automation of AI infrastructure, policy enforcement within automation platforms, and the unlocking of AI ops workflows. The discussion highlighted the use of event-driven automation, observability data, and the importance of standardizing infrastructure operations. Specific tools like Ansible, Kubernetes, and large language models were mentioned for simplifying AI deployments and remediation processes. The session also covered the use of AI for root cause analysis, remediation table generation, and the integration of AI with various observability platforms.

Action Items

[ ] Provide the diagrams shown in the slides to the participants.
[ ] Ensure everyone has access to the lab environment and can follow the lab instructions.
[ ] Emphasize the importance of following the lab instructions exactly, including using the suggested names for workflows.
[ ] Remind participants that all passwords are available on the landing page of the lab.
Outline

Automation and Virtualization in Networking

Speaker 1 discusses the importance of automation in networking, mentioning that even major networking vendors are now selling apps.
The conversation highlights the use of hybrid networking products and the need for automation.
AI is introduced as another domain, with potential integration into existing automation structures.
Speaker 1 outlines three main use cases for AI integration, starting with Red Hat AI.
AI Integration and Policy Enforcement

Speaker 1 explains the coordination of AI use cases, starting with Red Hat AI and providing mitigation for certified collections.
The discussion includes the automation of AI infrastructure and the unlocking of AI ops workflows.
A new feature, AI in check, is introduced, which allows policy enforcement within the automation platform.
The importance of event-driven automation in achieving comprehensive automation capabilities is emphasized.
Observability and Event-Driven Automation

Speaker 1 describes the observe, evaluate, and respond cycle in AI operations.
The conversation covers the integration of AI infrastructure with existing infrastructure to handle errors and automate remediation.
Simple use cases, such as ticket engagement and cost optimization, are discussed.
A customer example is provided, showing a reduction in service costs through event-driven automation.
AI Drilling Block and Infrastructure Standardization

Speaker 1 introduces the AI drilling block, focusing on standardizing infrastructure operations.
The discussion includes the use of Ansible to ease configuration and augment large language model requirements.
The integration of Kubernetes and other cloud platforms is highlighted.
The capability of Ansible to achieve one-click deployment of large language models is emphasized.
Coding Assistant and Platform Assistant

Speaker 1 explains the coding assistant, which helps in creating Ansible playbooks and understanding the Ansible automation platform.
The platform assistant, powered by a large language model, provides answers to questions about Ansible documentation.
The assistant can generate direct links to relevant documentation, making it easier for users to find information.
Future enhancements include the ability to take actions on behalf of users and handle more complex issues.
Event-Driven Workflows and Remediation

Speaker 1 describes the event-driven workflow, where events are observed and responded to using AI.
The conversation covers the integration of AI with existing infrastructure to handle errors and generate remediation plans.
The use of Ansible playbooks to execute remediation plans is explained.
The importance of standardizing workflows and avoiding manual steps is emphasized.
Lab Setup and User Access

Speaker 3 provides instructions for accessing the lab, including registration and password details.
The conversation covers the setup of the lab environment, including the integration of tools like Kafka and Service Bus.
The importance of following lab instructions to avoid failures is highlighted.
Speaker 3 mentions the availability of help from team members during the lab session.

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