Large Language Models in Production

I have extensive experience working with large language models in production environments, encompassing both API-based solutions and locally deployed systems.

Production Experience

API-Based LLM Integration

  • Integration of commercial LLM APIs into business workflows
  • Performance optimization and cost management for large-scale deployments
  • Building robust pipelines for real-time and batch processing
  • Error handling and fallback strategies for production reliability

Local LLM Deployment

  • Self-hosted model deployment and optimization
  • Hardware configuration and resource management
  • Model quantization and optimization techniques
  • Custom inference pipelines for specialized tasks

Technical Applications

Linguistic Analysis

  • Automated text processing and analysis workflows
  • Cross-lingual applications for historical linguistics research
  • Phonological and morphological pattern recognition
  • Integration with traditional computational linguistics tools

Data Processing

  • Large-scale text corpus processing and analysis
  • Automated annotation and classification systems
  • Quality assurance and validation frameworks
  • Reproducible research pipelines

Business Intelligence

  • Natural language interfaces for data analysis
  • Automated report generation and summarization
  • Content classification and organization systems
  • Customer interaction analysis and optimization

Technical Stack

APIs & Services:

  • OpenAI GPT models
  • Anthropic Claude
  • Google PaLM/Gemini
  • Custom API integrations

Local Deployment:

  • Llama 2/3 family models
  • Mistral models
  • Custom fine-tuned models
  • Quantized model optimization

Infrastructure:

  • Docker containerization
  • GPU acceleration (CUDA/ROCm)
  • Kubernetes orchestration
  • Cloud and on-premises deployment

Philosophy & Best Practices

My approach to LLM integration emphasizes:

  • Reliability: Robust error handling and monitoring
  • Efficiency: Cost-effective resource utilization
  • Reproducibility: Consistent results across deployments
  • Ethics: Responsible AI practices and bias mitigation
  • Security: Data privacy and model safety considerations

Research Integration

I actively explore the intersection of LLMs with computational historical linguistics, investigating applications in:

  • Automated analysis of historical texts
  • Cross-linguistic pattern detection
  • Phylogenetic hypothesis generation
  • Data augmentation for low-resource languages