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Performance Optimization for .NET 9 Applications on Azure
Results: Cold start reduced from 800ms to 150ms. These optimizations combined typically reduce Azure compute costs by 30-50% while improving response times.…
Optimization
33 articles
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Cost Management for Azure AI Services: Budgeting and Optimization
Azure AI costs depend on multiple factors: model selection, token usage, deployment type (serverless vs. provisioned), and regional pricing. Visibility into…
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AI FinOps: Managing AI Costs at Scale
AI FinOps provides visibility and control over AI infrastructure costs.
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AI Cost Optimization: Managing LLM Expenses
Strategic cost optimization can reduce AI expenses by 50-80% while maintaining quality.
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Speculative Decoding: Accelerating LLM Generation
Speculative decoding can achieve 2-3x speedup without any quality degradation.
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Quantization Techniques: Shrinking Models Without Losing Quality
Strategic quantization enables deploying large models on resource-constrained devices.
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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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Model Routing Patterns: Smart Query Distribution
Smart routing can reduce AI costs by 50-70% while maintaining quality.
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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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Prompt Caching Strategies: Reducing Latency and Cost
Smart caching can reduce AI costs by 30-50% for applications with repetitive queries.
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Context Window Management: Maximizing LLM Effectiveness
Strategic context management enables handling of complex, long-context scenarios.
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Vector Search Optimization: Scaling to Billions of Vectors
Quantization and HNSW tuning enable vector search at billion-scale with reasonable latency.
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Efficient Inference: Optimization Techniques for Production AI
Efficient inference is crucial for production AI. Apply these techniques systematically and measure the impact at each step.
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Inference Optimization: Making AI Fast and Cost-Effective
Inference optimization is a continuous process. Start with caching and routing for quick wins, then progressively implement more sophisticated techniques.
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Cost Controls for AI Applications: Budgeting and Optimization
Cost control in AI applications requires a multi-layered approach: track everything, set budgets, optimize requests, and review regularly. The strategies…
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Cost Management in Microsoft Fabric
1. Monitor regularly : Daily cost tracking enables early intervention 2. Set alerts : Budget alerts prevent surprises 3. Allocate costs : Understand which…
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Spark Optimization in Microsoft Fabric
1. Enable AQE : Adaptive Query Execution handles many optimizations automatically 2. Right size partitions : Target 128MB partitions 3. Broadcast small…
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Scalar Quantization for Vector Search: A Practical Guide
Scalar quantization maps floating point values to integers by dividing the value range into buckets: Understanding quantization error helps set…
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V-Order Optimization: Deep Dive into Fabric's Secret Weapon
V-Order feels like a secret weapon because it's a write-time optimisation with outsized read-time benefits. In practice I enable V-Order on wide, heavily…
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Direct Lake Optimization: Maximizing Power BI Performance
Direct Lake changes the Power BI performance story — but it's not automatic. Over the past months I've seen Direct Lake deliver dramatic improvements when…
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Fabric Performance Tuning: Optimizing Your Data Platform
Performance tuning in Fabric blends write-time optimisations (v-ordering, partitioning) with query-time strategies (predicate pushdown, materialised views).…
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Context Caching for LLM Applications
Tomorrow we'll explore conversation summarization techniques. Redis Caching Semantic Search LLM Caching Patterns
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Semantic Compression: Preserving Meaning with Fewer Tokens
Tomorrow we'll explore context caching strategies. Text Summarization Survey Sentence Transformers ROUGE Score
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GPU Optimization for LLM Workloads
Tomorrow we'll explore Azure ML compute options. PyTorch CUDA Semantics Flash Attention NVIDIA Optimization Guide
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Efficient Inference: Optimizing LLM Response Times
Tomorrow we'll explore batching strategies in detail. vLLM TensorRT LLM Speculative Decoding Paper
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Model Distillation: Creating Efficient Student Models
Tomorrow we'll dive deeper into knowledge distillation techniques. Distilling Knowledge in Neural Networks TinyBERT DistilBERT
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Context Pruning: Managing Token Limits in LLM Applications
Effective context pruning ensures your LLM applications work reliably. Tomorrow, I will cover token budgeting strategies.
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Fabric Capacity Metrics: Understanding and Optimizing Consumption
Fabric's capacity unit (CU) model is genuinely different from the per-resource billing in Synapse Analytics, and understanding it properly is the difference…
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Token Optimization Strategies for GPT-4
Output tokens cost 2x input tokens. This changes optimization strategy. With systematic token optimization, you can reduce GPT-4 costs by 50-70% while…
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Hyperparameter Tuning with Azure ML Sweep Jobs
Sweep jobs automate the tedious process of hyperparameter tuning, helping you find optimal configurations faster.
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Quantum Optimization: Solving Real-World Problems
Common optimization problems suitable for quantum: Traveling Salesman Problem (TSP) Vehicle Routing Portfolio Optimization Job Shop Scheduling Max Cut…
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Intelligent Query Processing in Azure SQL: Automatic Performance Optimization
IQP represents a significant step toward self-tuning databases, reducing the manual effort required to optimize query performance while automatically…
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Implementing Azure Advisor Recommendations at Scale
Azure Advisor is the free recommendation service that surfaces findings your team should have already found—and usually hasn't. The five categories (Cost…