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Introduction to Generative AI for FinOps
Overview of generative AI and its application in FinOps, presented by Rich Hoyer, a seasoned FinOps expert.
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Introduction to Generative AI in FinOps: Enhancing Financial Operations
Our Focus: Generative AI / NLP
Generative AI vs. Traditional Automation: A Comparative Analysis for FinOps
Deterministic Automation Versus AI: Demo Part I
Deterministic Automation Versus AI: Demo Part II
Deterministic Automation Versus AI: Demo Part III
Large Language Models (LLM) Details
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Prompt Engineering
Retrieval Augmented Generation (RAG) in FinOps
Fine Tuning your model
Options For Local Deployment
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Exploring Deployment Options for AI Models in FinOps
Exploring AI Model Deployment Options
Managing Deployment Challenges for AI Models
Embracing Serverless Solutions for FinOps: A Production Perspective
Key Considerations for Deploying Serverless Solutions
Application: Best Practice Codification
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Codifying and Centralizing FinOps Best Practices for Enhanced Efficiency
Setup of Bedrock For FinOps Knowledge Codification
Demonstration of FinOps Knowledge Codification: Cost Segmentation Practices
Demonstration of FinOps Knowledge Codification (Continued)
Demonstration of FinOps Knowledge Codification (Continued)
Demonstration of FinOps Knowledge Codification (Continued)
Demonstration of Code Snippet Retrieval
Application: Data Analytics & Integration
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Introduction to Natural Language SQL for Cloud Cost Analysis
Demo of Natural Language SQL in BigQuery
Demo of Natural Language SQL in BigQuery (Continued)
Conclusion
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Integrating LLMs With Multiple Platforms
Automating FinOps Procedures with AI for Proactive Cost Management
Final Thoughts: Harnessing the Power of Generative AI in FinOps
Generative AI for FinOps by Rich Hoyer
About Lesson

This section demonstrates the use of natural language SQL for querying data in BigQuery. It highlights the efficiency and ease of using natural language to generate SQL queries for data interrogation.

Key Takeaways:

  • Benefits of using natural language for SQL queries.
  • Example of generating SQL queries in BigQuery.
  • Enhancing data interrogation efficiency with natural language processing.
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