Production Deployment Strategies for AI Agents at Scale

Deploy AI agents to production with Kubernetes orchestration, OpenTelemetry observability, and cost management. Complete guide covering infrastructure patterns, distributed tracing, monitoring strategies, and enterprise deployment on Azure, AWS, and GCP.

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Repository Intelligence: Microsoft’s AI Revolution That Understands Your Entire Codebase, Not Just Lines of Code

Explore how Microsoft’s repository intelligence transforms AI coding assistants from simple autocomplete tools into context-aware development partners that understand your entire codebase, relationships, and history.

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Production Operations and Distributed Deployment: Monitoring, Versioning, and Maintaining Edge AI at Scale

Comprehensive production operations guide for distributed edge AI deployments. Covers Prometheus/Jaeger monitoring integration, data drift detection with statistical analysis, model versioning and registry management, canary deployment with automated rollback, OTA update orchestration, and fleet management patterns for 100+ edge devices.

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Azure AI Foundry with Anthropic Claude Part 2: Deployment Fundamentals – Complete Step-by-Step Guide

Step-by-step guide to deploying Claude models in Azure AI Foundry. Learn how to create AI Foundry hubs and projects, deploy Claude Sonnet 4.5, Opus 4.5, and Haiku 4.5, configure Microsoft Entra ID or API key authentication, verify deployments, understand rate limits and quotas, and implement deployment best practices for production environments.

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Azure Monitor with OpenTelemetry Part 7: Production Monitoring and Observability Patterns

Master production observability with OpenTelemetry and Azure Monitor. Learn intelligent sampling strategies, actionable alerting patterns, performance optimization, cost management, operational dashboards, and incident response integration for enterprise-scale applications.

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Model Context Protocol Part 6: Production Deployment and Monitoring at Scale

Master production deployment of MCP servers with Kubernetes orchestration, CI/CD automation, OpenTelemetry monitoring, and performance optimization strategies for enterprise-scale AI integration.

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Vector Databases Part 7: Production Deployment Patterns and Operations

Moving vector databases from development to production requires addressing challenges that prototype implementations ignore including high availability, disaster recovery, cost optimization, and operational monitoring. Production

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Azure AI Foundry Deep Dive Series Part 2: Building Production AI Applications with Enterprise Architecture

Learn how to build production-ready AI applications using Azure AI Foundry. This comprehensive guide covers architecture patterns, security implementation, cost optimization strategies, and operational best practices for enterprise deployments.

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