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2026 Predictions: The Rise of Multimodal AI Applications
As 2025 closes, multimodal AI has moved from impressive demos to practical applications. Here are my predictions for how multimodal capabilities will…
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 9 of 92
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Kubernetes Cost Optimization on AKS: Lessons from 2025
Kubernetes cost optimization is continuous. Review these metrics weekly and adjust based on actual usage patterns.
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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.…
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Building Production RAG Systems: Best Practices for 2026
Production RAG is iterative. Start simple, measure everything, and improve based on real user feedback.
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Top 10 Azure Data Services Lessons from 2025
After a year of implementing data solutions on Azure, I've compiled the most important lessons that can save you time, money, and headaches. These come from…
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Migrating from LangChain to Semantic Kernel: A Practical Guide
Many teams that started with LangChain in 2024 are now evaluating Semantic Kernel for its tighter Azure integration and production-ready features. Having…
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Azure OpenAI vs OpenAI Direct: A 2025 Comparison Guide
One of the most common questions I receive from enterprise clients is whether to use Azure OpenAI Service or OpenAI's API directly. After working with both…
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2025 AI Year in Review: The Year Agents Became Real
The most significant shift this year was the maturation of autonomous AI agents. What started as experimental frameworks evolved into production-ready…
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November 2025 Recap: Ignite Announcements and AI Progress
Azure AI Foundry unified the AI development experience, combining model catalog, deployment tools, and prompt management into a single platform. This…
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Preparing for 2026: AI Technology Trends and Planning
Starting 2026 planning now ensures your organization can capitalize on AI advances while managing risks effectively. The key is balancing ambitious goals…
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AI-Powered Code Review: Integrating LLMs into Development Workflows
While linters catch syntax issues, AI reviewers understand context, identify logical errors, suggest architectural improvements, and explain complex code…
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Data Governance in Microsoft Fabric: Purview Integration and Compliance
Organizations struggle with data sprawl, unclear ownership, inconsistent definitions, and regulatory requirements. A unified governance approach addresses…
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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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Building Multi-Agent Systems with AutoGen and Azure
Instead of a single LLM handling all tasks, multi-agent systems divide work among specialized agents that communicate and coordinate to achieve goals.
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Document Intelligence: Extracting Structured Data from Complex Documents
Document Intelligence understands document structure, extracting tables, key-value pairs, and semantic entities rather than just raw text. This structured…
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Azure Container Apps: Serverless Deployment for AI Workloads
AI applications often have variable load patterns with periods of high demand followed by quiet periods. Container Apps scales to zero during idle times…
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Semantic Caching for LLM Applications: Reducing Costs and Latency
Traditional caching requires exact matches. Users asking "What is Azure?" and "Can you explain Azure?" would generate two separate API calls. Semantic…
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Implementing Guardrails for Production LLM Applications
LLMs can generate harmful content, leak sensitive information, or be manipulated through prompt injection. A layered defense approach protects users and…
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Vector Database Selection: Comparing Azure AI Search, Cosmos DB, and PostgreSQL
Azure AI Search, Cosmos DB with vector indexing, and Azure Database for PostgreSQL with pgvector each provide vector search capabilities with different…
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Monitoring AI Applications: Observability Patterns for LLM Systems
LLM systems have unique characteristics: non-deterministic outputs, variable latency, complex cost models, and quality metrics that require semantic…
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Building Data Lakehouses: Medallion Architecture Best Practices
Each layer serves a specific purpose in the data refinement pipeline. Bronze captures raw data, silver cleans and conforms it, and gold delivers…
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Ignite 2025 Day 3: Copilot Extensions and Agent Framework
The Copilot agent framework represents a significant step toward enterprise AI automation, enabling organizations to build intelligent assistants that work…
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Ignite 2025 Day 2: Microsoft Fabric Innovations and Real-Time Intelligence
These Fabric updates demonstrate Microsoft's commitment to building a truly unified data platform that handles real-time and batch workloads while…
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Ignite 2025 Day 1: Azure AI Foundry and New Model Capabilities
The Azure AI Foundry represents Microsoft's vision for enterprise AI development - a unified platform that handles the complexity of model selection…