# Building and Deploying LLM-Based Recommendation Systems in Private Equity ## Background and Context This case study presents insights from a data scientist's journey in implementing LLM-based recommendation systems at a private equity company. The speaker, Annie, shares her transition from traditional data science to working with LLMs in production, highlighting the challenges and considerations involved in deploying LLM-based solutions. ## Business Requirements and Challenges - Primary business requirement: Users need to find a match within the first five recommendations - Target audience considerations: ## Technical Implementation ### Data Science Lifecycle Integration - Traditional linear data science approach had to be adapted for LLMs - Current stage focuses on inference rather than fine-tuning or RAG - LLMs integrated into nearly every step of the data science lifecycle - Primary use cases: ### Production Considerations and Challenges ### API and Resource Management - Using OpenAI API for data cleaning processes - Key considerations: ### Data Quality and User Experience - Challenges in collecting quality input data: - Evaluation considerations: ### Deployment Architecture ### Cloud Infrastructure - AWS-based deployment strategy - Key components: ### Implementation Benefits - No need to download model artifacts - Direct model invocation through Bedrock runtime - Simplified deployment process through cloud integrations ## Lessons Learned and Best Practices ### Key Questions for LLM Production Deployment - Processing capacity and scalability considerations - Compute requirements for various LLM operations - Evaluation metrics and data collection strategies - Input data quality assurance - Automation capabilities and limitations ### Technical Recommendations - Utilize cloud service integrations for simplified deployment - Consider serverless architectures for scalability - Implement proper evaluation frameworks - Plan for automated data processing pipelines - Account for resource limitations and costs ### Implementation Strategy - Start with available cloud services and integrations - Utilize pre-built solutions where possible - Test deployment limits in controlled environments - Focus on practical implementation rather than perfect solutions ## Future Considerations ### Scalability and Growth - Planning for increased user load - Managing growing data processing requirements - Optimizing cost structures - Improving recommendation accuracy ### Development Opportunities - Exploring fine-tuning capabilities - Implementing RAG systems - Enhancing evaluation frameworks - Improving user input collection methods ## Conclusion The implementation of LLM-based recommendation systems presents significant challenges that go beyond the typical data science workflow. Success requires careful consideration of: - Technical infrastructure - Resource management - User experience - Data quality - Evaluation metrics - Deployment strategies While there's no "quick and dirty" solution for deploying LLMs in production, utilizing cloud services and following structured approaches can help organizations successfully implement LLM-based solutions. The key is to start with practical implementations while considering all aspects of production deployment, from data processing to user experience.