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Azure Machine Learning Managed Endpoints: Deploying Models with Zero Downtime
Configure Azure Monitor alerts on latency and error rates. Implement automatic rollback triggers when thresholds are breached.
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 15 of 92
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Implementing Guardrails for Production LLM Applications
Guardrails are essential for enterprise AI deployments, protecting both users and your organization.
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GitHub Copilot Workspace: AI-Powered Development Environment Deep Dive
The workspace analyzes your codebase, identifies affected files, and proposes a complete implementation plan before writing any code.
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Fine-Tuning GPT-4o for Domain-Specific Tasks on Azure
After training completes, evaluate on a held-out test set before deploying. Monitor the fine-tuned model's performance against the base model to ensure…
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Evaluating RAG Systems: Metrics and Automated Testing with Azure AI
Fail builds when quality metrics drop below thresholds, catching regressions before they reach production.
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Semantic Kernel v2: Building Composable AI Applications in C#
Semantic Kernel v2 brings enterprise-grade patterns to AI development, making it easier to build maintainable, testable AI applications.
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Building Multi-Agent Systems with Azure AI Foundry Orchestration
Azure AI Foundry provides trace visualization showing the complete agent interaction graph, making it easy to debug complex workflows and optimize handoff…
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Fabric Data Pipelines: Orchestrating ML Feature Engineering at Scale
For real-time inference, query the feature store directly from your model serving endpoint. Fabric's query optimization ensures millisecond-level feature…
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Prompt Caching with Claude and Azure OpenAI: Reducing Latency and Costs
Track cache hit rates in your monitoring. Applications with high context reuse typically see 60-80% cache hit rates, dramatically reducing per-request costs.
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Cosmos DB Vector Search: Implementing Semantic Search at Global Scale
Configure your Cosmos account with write regions near your users and read replicas globally. Vector searches automatically route to the nearest replica…
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Real-Time AI Inference with Azure Container Apps: Dynamic Scaling Patterns
Use spot instances for non-critical workloads, implement request batching for throughput optimization, and configure appropriate scale-to-zero policies.…
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Azure AI Search Vector Optimization: Reducing Costs While Improving Recall
Vector search costs can spiral quickly at scale. After optimizing Azure AI Search deployments processing 50 million vectors, I've identified key patterns…
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Copilot Extensibility: Building Custom Plugins for Microsoft 365 Copilot
Use the Copilot Developer Portal to test plugins in a sandboxed environment before enterprise deployment. The new debugging tools show exactly how Copilot…
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GPT-4o Vision for Document Intelligence: Building Smart OCR Pipelines
For multi-page documents, process pages in parallel and use a synthesis step to merge extracted data. GPT-4o handles cross-page references like "continued…
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Claude 3.5 Sonnet for Code Review: Automated PR Analysis Patterns
Claude 3.5 Sonnet has become my go-to model for automated code review workflows. Its exceptional ability to understand context across large codebases makes…
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Microsoft Fabric Real-Time Intelligence: Streaming Analytics at Scale
Microsoft Fabric's Real-Time Intelligence workload has matured significantly since GA. Today I'm exploring how to build streaming analytics pipelines that…
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Building AI Agents with Azure AI Foundry: A Post-Build 2025 Implementation Guide
The new agent framework introduces several critical improvements. First, native multi-agent orchestration allows agents to collaborate without custom…
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June Recap: Building Modern AI Systems
Understanding june recap is essential for production AI systems. Here's what you need to know.
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Data Lineage: Building Modern AI Systems
Understanding data lineage is essential for production AI systems. Here's what you need to know.
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Metadata Management: Building Modern AI Systems
Understanding metadata management is essential for production AI systems. Here's what you need to know.
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Data Governance: Building Modern AI Systems
Understanding data governance is essential for production AI systems. Here's what you need to know.
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Unity Catalog: Building Modern AI Systems
Understanding unity catalog is essential for production AI systems. Here's what you need to know.
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Iceberg Ai: Building Modern AI Systems
Understanding iceberg ai is essential for production AI systems. Here's what you need to know.
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Delta Lake Ai: Building Modern AI Systems
Understanding delta lake ai is essential for production AI systems. Here's what you need to know.