Microsoft Foundry Local brings the power of Azure AI Foundry directly to your local device, enabling you to run state-of-the-art AI models without cloud dependencies. Announced at Microsoft Build 2025 and continuously enhanced since, Foundry Local represents a paradigm shift in how developers can build AI-powered applications—with complete data privacy, zero API costs, and offline […]
Read more →Category: Backend Development
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 […]
Read more →Streaming Responses for LLMs: Implementing Server-Sent Events
Streaming LLM responses dramatically improves user experience. After implementing streaming for 20+ LLM applications, I’ve learned what works. Here’s the complete guide to implementing Server-Sent Events for LLM streaming. Figure 1: Streaming Architecture Why Streaming Matters Streaming LLM responses provides significant benefits: Perceived performance: Users see results immediately, not after 10+ seconds Better UX: Progressive […]
Read more →.NET AI Performance Optimization: Reducing Latency and Costs
Last year, I inherited a .NET AI application that was struggling. Response times averaged 2.3 seconds, costs were spiraling, and users were complaining. After three months of optimization, we cut latency by 87% and reduced costs by 72%. Here’s what I learned about optimizing .NET AI applications for production. Figure 1: .NET AI Performance Optimization […]
Read more →ML.NET for Custom AI Models: When to Use ML.NET vs Cloud APIs
Six months ago, I faced a critical decision: build a custom ML model with ML.NET or use cloud APIs. The project required real-time fraud detection with zero latency tolerance. Cloud APIs were too slow. ML.NET was the answer. But when should you use ML.NET vs cloud APIs? After building 15+ production ML systems, here’s what […]
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