2025 taught enterprise technology leaders a critical lesson: infrastructure readiness matters more than model capability. This year-end review explores platform engineering, data governance, healthcare AI breakthroughs, and five predictions for 2026.
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Case Study: Building a Modern FHIR Patient Timeline Explorer with .NET 10 and React 19
Executive Summary This case study explores the development of DooLittle Health Patient Timeline Explorer, a modern healthcare application that demonstrates enterprise-grade architecture patterns for FHIR-compliant patient data visualization. Built as a proof-of-concept, this project showcases best practices in full-stack development, cloud-native deployment, and healthcare interoperability standards. 🏥 HEALTHCARE INTEROPERABILITY SERIES This article is part of […]
Read more →Building Interoperable Healthcare Data Systems for AI: A Complete Guide to FHIR, Standards, and Governance
Healthcare AI fails when data remains siloed. This article explores how FHIR, SNOMED CT, and platform thinking enable interoperable healthcare data systems for AI at scale, with insights from EU, UK, and Ireland initiatives.
Read more →Azure Container Apps Dynamic Sessions: Secure Code Execution for AI Agents
AI agents that can write and execute code introduce significant security risks—from data exfiltration to resource abuse. Azure Container Apps Dynamic Sessions provides a solution: ephemeral, sandboxed execution environments that isolate agent-generated code from your production infrastructure. This comprehensive guide explores how to implement secure code execution for AI code interpreters, automated testing agents, and […]
Read more →Data Quality for AI: Ensuring High-Quality Training Data
Data quality determines AI model performance. After managing data quality for 100+ AI projects, I’ve learned what matters. Here’s the complete guide to ensuring high-quality training data. Figure 1: Data Quality Framework Why Data Quality Matters Data quality directly impacts model performance: Accuracy: Poor data leads to poor predictions Bias: Biased data creates biased models […]
Read more →Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python
After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]
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