Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just producing text responses, models can now decide when to call external functions, APIs, or tools to accomplish tasks. This capability enables building assistants that can search the web, query databases, send emails, execute code, and interact with any system that exposes […]
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CDA (Clinical Document Architecture): The XML Standard for Medical Documents
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
Read more →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.
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