Doctolib, a leading European e-health service provider, is undertaking a significant transformation of their data platform to incorporate advanced AI and LLM capabilities. This case study explores their architectural evolution and implementation of LLMOps practices within a healthcare context. ## Overall Platform Context and Evolution Doctolib's data platform has grown from supporting a small startup to serving a scale-up with over a hundred team members. The company is now transitioning from a purely reporting-focused platform to becoming a leader in AI for healthcare. This transformation requires a complete rebuild of their data infrastructure to support both traditional analytics and AI/ML workloads. ## LLMOps and AI Infrastructure The new architecture includes several key LLMOps components designed to support the entire lifecycle of large language models in a healthcare context: ### Training Infrastructure The platform includes a comprehensive ML Training Platform with specific LLMOps tooling designed for healthcare applications. This includes: * A dedicated Model Registry for version control and lifecycle management of LLMs * A Feature Store for managing and serving features to models * Specialized LLMOps tooling for model fine-tuning, deployment, monitoring, and versioning * Prompt optimization and cost management capabilities * An annotation platform for collaborative labeling of healthcare data * ML experiment tracking for maintaining records of training runs and results * High-performance training workstations optimized for LLM training ### Inference and Deployment The platform implements a robust inference infrastructure including: * Multiple LLM provider integrations for flexibility in model selection * Comprehensive model monitoring for production deployments * Scalable model serving capabilities * An optimized inference engine supporting various hardware backends * A Model-as-a-Service catalog for pre-trained healthcare-specific models ### Data Quality and Governance Given the sensitive nature of healthcare data, the platform implements strict governance: * Healthcare-specific ontology management supporting HL7, FHIR, OMOP, and DICOM standards * Robust data masking and encryption * Column and row-level access controls * Comprehensive audit logging * Data quality monitoring and validation ### User Interface and Interaction The platform includes several user-facing components: * A GenAI Assistant enabling natural language data exploration for non-technical users * Self-service dashboards enhanced with AI capabilities * Collaborative workspace environments for data scientists and ML engineers ## Implementation Approach and Team Structure The implementation is being handled by four specialized teams: * Data Engineering Platform team managing infrastructure foundations * Data Ingestion & Output team handling data flow * Data Tools team providing accessibility and usability tools * ML Platform team specifically focused on model development and deployment ## Technical Considerations and Challenges The platform addresses several critical challenges in implementing LLMs in healthcare: * Security and compliance requirements for sensitive health data * Integration with existing healthcare systems and standards * Scalability needs for handling large-scale health data * Cost optimization for compute-intensive LLM operations * Model monitoring and validation in a healthcare context ## Vector Database and Embedding Infrastructure The platform includes specialized storage for AI workloads: * A dedicated vector database for storing and managing embeddings * Optimization for similarity searches in high-dimensional spaces * Support for multimodal healthcare data including text, images, and structured data ## Quality Assurance and Monitoring The implementation includes comprehensive quality measures: * Automated testing and validation pipelines * Performance monitoring and optimization * Data quality checks and validation * Model performance tracking and drift detection ## Innovation and Future-Proofing The architecture is designed to be extensible and adaptable: * Support for multiple LLM providers and model types * Flexible infrastructure that can accommodate new AI technologies * Scalable architecture that can grow with increasing data volumes * Integration capabilities for future healthcare AI applications The platform represents a significant advancement in healthcare LLMOps, demonstrating how large language models can be effectively deployed in a highly regulated industry while maintaining security, compliance, and performance standards. The architecture shows careful consideration of healthcare-specific requirements while implementing modern LLMOps practices.