LATAM Airlines, South America's largest airline with approximately 350 aircraft and 1600 daily departures, has undergone a significant digital transformation by implementing a comprehensive MLOps strategy. This case study demonstrates how a traditional airline company successfully integrated both conventional machine learning and LLM technologies into their operations through a carefully designed MLOps framework called Cosmos. # Organizational Context and Strategy LATAM Airlines approached their AI transformation with a clear vision from their CEO: to make data their competitive edge in an industry where revolutionary changes in aircraft or fuel technology are not expected in the near term. The company adopted a hybrid organizational structure that balances centralized MLOps expertise with domain-specific knowledge: * A centralized MLOps team maintains and develops the Cosmos framework, standardizing tools and development practices * Decentralized teams in specific business domains (maintenance, finance, web operations, etc.) implement solutions using the framework * Analytics translators serve as bridges between technical teams and business units, ensuring proper change management and adoption This structure has proven effective in reducing the time to deploy models from 3-4 months to less than a week, while ensuring proper governance and business alignment. # Technical Infrastructure: The Cosmos Framework Cosmos is designed as a developer-centric, open-source framework that prioritizes speed while minimizing bureaucracy. Key technical characteristics include: * Vendor-agnostic architecture, though currently heavily integrated with Google Cloud Platform (GCP) * Custom wrappers around cloud services to maintain flexibility and portability * Strict environment isolation (development, integration, production) * Support for both traditional ML and LLM deployments * Integration with monitoring tools like Spotify's Backstage * Comprehensive CI/CD implementation using Cloud Build # LLM Integration and Use Cases The case study presents several production implementations, with particular interest in how LLMs are integrated into their operations: ## Personalized Travel Recommendations * LLMs are used to generate destination category scores (beach quality, nightlife, safety, nature, etc.) * These LLM-generated features are combined with traditional ML models for personalized recommendations * Real-time API integration with the website backend * A/B testing and monitoring through event tracking and causal inference ## Additional Production Use Cases * Extra Fuel Optimization: ML models predict optimal fuel reserves, saving millions of dollars and reducing CO2 emissions * Inventory Management: 60,000+ models predict spare part demands across airports * Various chatbots and classification systems integrated through the framework # MLOps Practices and Considerations The framework implements several notable MLOps practices: * Data Quality and Monitoring * Comprehensive monitoring for data drift * Quality checks throughout the pipeline * Integration with business metrics and KPIs * Separate measurement team for impact analysis * Development and Deployment * Automated CI/CD pipelines * Environment isolation * Data policies for sensitive information handling * Integration testing capabilities * Production Support * Real-time and batch processing capabilities * Low-latency API services * Monitoring dashboards * Alert handling systems # Challenges and Lessons Learned The case study highlights several important lessons: * Integration with existing systems often takes longer than model development * Change management and cultural transformation are crucial in traditional industries * Having dedicated analytics translators helps bridge technical and business perspectives * Vendor lock-in should be carefully managed through abstraction layers * Business unit ownership and responsibility are key to successful model adoption # Results and Impact The implementation has shown significant business impact: * Millions of dollars in cost savings through fuel optimization * Reduced inventory costs while maintaining parts availability * Improved customer experience through personalization * Faster model deployment (from months to weeks) * Successfully transformed a traditional airline into a more data-driven organization The case study demonstrates that even large, traditional companies can successfully implement sophisticated MLOps practices when they have clear leadership support, appropriate organizational structure, and well-designed technical infrastructure. The combination of traditional ML and LLM capabilities, supported by robust MLOps practices, has enabled LATAM Airlines to achieve significant operational improvements and cost savings while maintaining the flexibility to adapt to new technologies and requirements.