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Efficient Inference Patterns: Maximizing AI Throughput
Efficient inference patterns can improve throughput by 5-10x while reducing costs.
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 19 of 92
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Browser AI with WebGPU: Running Models in the Browser
WebGPU brings near-native AI performance to web applications with zero installation.
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Mobile AI Development: Building Intelligent Mobile Apps
Mobile AI brings powerful intelligence to users while respecting privacy and working offline.
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Edge AI Architecture: Designing for On-Device Intelligence
Edge AI architecture balances latency, privacy, and capability across processing tiers.
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ONNX Deployment: Cross-Platform AI Model Deployment
ONNX enables train-once-deploy-anywhere for AI models across diverse hardware.
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Model Routing Patterns: Smart Query Distribution
Smart routing can reduce AI costs by 50-70% while maintaining quality.
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SLM vs LLM Strategies: Choosing the Right Model Size
The right model size depends on your specific requirements, not just capabilities.
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Phi-3 Local Deployment: Running Microsoft's SLM On-Device
Phi-3 enables powerful AI capabilities entirely on-device with no cloud dependency.
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NPU Programming: Leveraging Neural Processing Units
NPUs enable efficient on-device AI with 10-100x better power efficiency than CPUs.
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Windows AI Features: Building for the AI PC
Windows AI brings powerful on-device intelligence to every application.
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Copilot Extensibility: Building Custom Copilot Experiences
Custom Copilot plugins bring your business data into the AI conversation.
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Build 2025 Preview: What to Expect from Microsoft's Developer Conference
Build 2025 will likely be the most AI-focused Build ever. Prepare to learn and adapt quickly.
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March 2025 Recap: Advanced Patterns and Production Readiness
Query transformation techniques Reranking strategies for precision Chunking optimization approaches Context window management Knowledge graphs with AI…
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Q1 2025 AI Retrospective: Key Developments and Learnings
Q1 laid the foundation for production AI. Q2 will be about scaling and governance.
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Audio AI Applications: Speech, Music, and Sound Processing
Audio AI enables hands-free interfaces and automated analysis of voice content.
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Vision-Language Models: Understanding Images with AI
Vision-language models enable applications from document understanding to visual inspection.
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Multi-Modal AI Applications: Beyond Text
Multi-modal AI opens up applications from document processing to video analysis.
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Model Distillation: Creating Efficient Specialized Models
Distillation enables production deployment with 10x cost reduction while maintaining quality.
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LLM Fine-Tuning on Azure: When and How to Customize Models
Fine-tuning is a powerful tool when used appropriately, but prompt engineering often achieves similar results faster.
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A/B Testing AI Features: Data-Driven AI Improvements
Rigorous experimentation enables confident AI improvements based on real user impact.
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Evaluation Metrics for AI: Measuring What Matters
Regular evaluation with consistent metrics drives continuous improvement in AI quality.
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AI Observability: Monitoring LLM Applications in Production
Comprehensive observability enables continuous improvement of AI applications.
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Prompt Injection Defense: Protecting AI Applications
Defense in depth with multiple layers provides the best protection against prompt injection.
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Guardrails for LLMs: Building Safe AI Applications
Comprehensive guardrails are essential for responsible AI deployment.