What is CDA and Why It Matters CDA Document Structure Sample CDA Document Structure .NET CDA Parsing Implementation CDA Document Generation Common CDA Sections (C-CDA) CDA vs FHIR Documents Standards and References Related Articles in This Series Conclusion
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Enterprise Observability on Google Cloud: Mastering Logging, Monitoring, and Distributed Tracing
Introduction: Google Cloud’s operations suite (formerly Stackdriver) provides comprehensive observability through Cloud Logging, Cloud Monitoring, Cloud Trace, and Error Reporting. This guide explores enterprise observability patterns, from log aggregation and custom metrics to distributed tracing and intelligent alerting. After implementing observability platforms for organizations running thousands of microservices, I’ve found GCP’s integrated approach delivers exceptional […]
Read more →Structured Output from LLMs: Instructor Library and Production Patterns (Part 2 of 2)
Introduction: Getting LLMs to return structured data instead of free-form text is essential for building reliable applications. Whether you need JSON for API responses, typed objects for downstream processing, or specific formats for data extraction, structured output techniques ensure consistency and parseability. This guide covers the major approaches: JSON mode, function calling, the Instructor library, […]
Read more →LLM Deployment Strategies: From Model Optimization to Production Scaling
Introduction: Deploying LLMs to production is fundamentally different from deploying traditional ML models. The models are massive, inference is computationally expensive, and latency requirements are stringent. This guide covers the strategies that make LLM deployment practical: model optimization techniques like quantization and pruning, inference serving with batching and caching, containerization with GPU support, auto-scaling based […]
Read more →Python Machine Learning Frameworks: Scikit-learn, TensorFlow, and PyTorch Compared
Compare Python’s leading ML frameworks for enterprise deployments. Learn when to use Scikit-learn for classical ML, TensorFlow for production deep learning, and PyTorch for research flexibility with production-ready code examples.
Read more →LLM Fine-Tuning Techniques: From LoRA to Full Parameter Training
Introduction: Fine-tuning transforms general-purpose LLMs into specialized models that excel at your specific tasks. While prompting can get you far, fine-tuning unlocks capabilities that prompting alone cannot achieve: consistent output formats, domain-specific knowledge, reduced latency from shorter prompts, and behavior that would require extensive few-shot examples. This guide covers the practical aspects of LLM fine-tuning: […]
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