Real-time data streaming has become essential for modern enterprises that need to process millions of events per second while maintaining low latency and high reliability. Azure Event Hubs stands as Microsoft’s fully managed, big data streaming platform, designed to handle massive throughput scenarios that traditional messaging systems simply cannot address. Having architected numerous streaming solutions […]
Read more →Month: February 2025
Batch Inference Optimization: Maximizing Throughput and Minimizing Costs
Introduction: Batch inference optimization is critical for cost-effective LLM deployment at scale. Processing requests individually wastes GPU resources—the model loads weights once but processes only a single sequence. Batching multiple requests together amortizes this overhead, dramatically improving throughput and reducing per-request costs. This guide covers the techniques that make batch inference efficient: dynamic batching strategies, […]
Read more →GitOps with a comparison between Flux and ArgoCD and which one is better for use in Azure AKS
GitOps has emerged as a powerful paradigm for managing Kubernetes clusters and deploying applications. Two popular tools for implementing GitOps in Kubernetes are Flux and ArgoCD. Both tools have similar functionalities, but they differ in terms of their architecture, ease of use, and integration with cloud platforms like Azure AKS. In this blog, we will […]
Read more →Mastering Hybrid Cloud with Google Anthos: Unified Kubernetes Management Across Any Environment
Introduction: Google Anthos provides a unified platform for managing applications across on-premises data centers, Google Cloud, and other cloud providers. This comprehensive guide explores Anthos’s enterprise capabilities, from GKE Enterprise and Config Management to Service Mesh and multi-cluster networking. After implementing hybrid cloud architectures for enterprises with complex compliance and data residency requirements, I’ve found […]
Read more →LLM Monitoring and Alerting: Building Observability for Production AI Systems
Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in […]
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