# State of Production Machine Learning and LLMOps in 2024 ## Overview This comprehensive case study presents insights from a keynote presentation on the current state of production machine learning and LLMOps in 2024. The speaker, who serves as Director of Engineering Science and Product, Scientific Adviser at The Institute for Ethical AI, and ACM member, provides a detailed analysis of the challenges and trends in implementing ML systems at scale. ## Key Motivations and Challenges - Model lifecycle extends beyond training - Production ML specific challenges ## Technological Trends ### Frameworks and Tools - Evolution from simple ML frameworks to complex MLOps ecosystems - Multiple tools available for different aspects: ### Architecture Components - Experimentation systems - Runtime engines - Code versioning - Model artifact management - Data pipeline management ### Monitoring and Observability - Traditional performance metrics - Advanced monitoring capabilities - Actionable insights through alerting systems - Aggregate performance analysis ### Security Considerations - End-to-end security coverage needed - Vulnerabilities present throughout ML lifecycle: - Working group at Linux Foundation focusing on ML/MLOps security ## Organizational Trends ### Development Lifecycle Evolution - Transition from SDLC to MLDLC (Machine Learning Development Lifecycle) - More flexible approach needed compared to traditional software - Risk assessments and ethics board approvals may be required - Custom governance based on use case ### Team Structure and Composition - Cross-functional teams becoming more common - Multiple personas involved: ### Scaling Considerations - Progressive scaling of automation and standardization - Ratio between data scientists and other roles evolves with scale - Infrastructure needs grow with number of models ## Data Management and Operations ### Data-Centric Approach - Shift from model-centric to data-centric thinking - Complex architectures combining offline and online components - Importance of data mesh architectures - Integration of MLOps with DataOps ### Metadata Management - Interoperability between different stages - Tracking relationships between: ## Best Practices and Recommendations ### Risk Management - Proportionate risk assessment needed - Consider impact levels when deploying - Human-in-the-loop options for higher risk scenarios - Not everything requires AI solution ### Production Readiness - Calculate risks before deployment - Consider sandboxing for new deployments - Evaluate compliance requirements - Set appropriate KPIs and SLOs ### Infrastructure Planning - Scale infrastructure based on use case needs - Consider both batch and real-time serving requirements - Plan for high-throughput and low-latency scenarios - Account for large model requirements ## Future Considerations - Growing importance of responsible AI development - Increasing regulatory requirements - Need for standardization across industry - Evolution of team structures and roles - Integration of LLMs into existing MLOps practices - Focus on human impact of ML systems