2 min read
Monitoring AI Applications in Production
This is how most AI deployments start. Here's how to fix it. Traditional apps: request comes in, response goes out. Monitor latency, errors, throughput. Done.
Monitoring
62 articles
1 min read
Implementing Observability for AI Applications with OpenTelemetry
Observability is essential for AI applications. Without it, you're flying blind on costs, performance, and quality. Implement these patterns early and…
1 min read
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…
1 min read
AI Observability with Azure Monitor and Application Insights
Monitor these essential AI metrics: latency (p50, p95, p99), token consumption per request, error rates by model and endpoint, cache hit rates for…
1 min read
Observability for LLM Applications: Tracing, Metrics, and Debugging
Log prompts and responses for failed interactions to a secure store for debugging. Implement sampling for successful calls to control storage costs while…
1 min read
AI Drift Detection: Monitoring Model Performance Over Time
Proactive drift detection prevents silent AI performance degradation.
1 min read
Continuous Evaluation: Monitoring AI Quality in Production
Continuous evaluation maintains AI quality standards throughout the system lifecycle.
1 min read
AI Monitoring Dashboards: Visualizing AI System Health
Comprehensive monitoring dashboards enable proactive AI operations management.
1 min read
AI Observability: Monitoring LLM Applications in Production
Comprehensive observability enables continuous improvement of AI applications.
1 min read
Building Custom LLM Observability: DIY Solutions
Building custom observability gives you full control over your data and features. Start simple and add complexity as your needs grow.
1 min read
Arize AI for Production LLM Monitoring
Arize provides comprehensive production monitoring for LLMs with enterprise features like drift detection, alerting, and detailed analytics. It's ideal for…
1 min read
LLM Observability Tools: Comparing the Landscape
Choose your observability tools based on your team size, budget, privacy requirements, and existing tooling. Start simple and add more sophisticated tools…
1 min read
Agent Observability: Monitoring AI Systems in Production
Observability transforms AI agents from black boxes into understandable systems. Combine metrics, logs, and traces to gain complete visibility into agent…
1 min read
Usage Reporting in Microsoft Fabric
1. Collect comprehensively : Gather data from all sources 2. Aggregate appropriately : Different audiences need different views 3. Automate distribution :…
1 min read
Monitoring AI Systems in Production
AI systems require specialized monitoring beyond traditional application metrics. This guide covers comprehensive observability for production AI.
1 min read
Query Insights in Fabric: Monitoring and Optimization
Tomorrow we'll explore Fabric Real Time Analytics. Query Insights Documentation Performance Tuning Monitoring DMVs
1 min read
Fabric Monitoring: Observability for Your Data Platform
One of my first questions when evaluating any data platform for enterprise use is "how do I see what's actually running?" — and in the early Fabric…
1 min read
Observability and Monitoring: Practices That Worked in 2022
Observability in 2022 moved beyond simple monitoring to true understanding of system behavior. OpenTelemetry became the standard for instrumentation. SLOs…
1 min read
Prediction Drift Monitoring for ML Models
Prediction drift monitoring provides early warning of model issues without waiting for ground truth labels.
1 min read
Concept Drift Detection in Machine Learning
Detecting concept drift enables timely model retraining to maintain prediction accuracy over time.
1 min read
Data Drift Detection for ML Models
Early drift detection enables proactive model maintenance and prevents silent failures in production.
1 min read
Model Monitoring in Production with Azure ML
Comprehensive monitoring ensures your ML models maintain their performance and reliability in production.
1 min read
Azure Log Analytics Integration with Synapse
Configure diagnostic settings to export to Data Lake: Use serverless SQL to query exported logs: Complex log analysis: Process logs with Azure Functions:…
1 min read
Azure Metrics Advisor: Intelligent Anomaly Detection for Time Series Data
The service provides: Smart anomaly detection : ML powered detection without manual threshold tuning Root cause analysis : Automatic correlation across…
1 min read
Prometheus Remote Write to Azure Monitor: Unified Metrics
Prometheus remote write to Azure Monitor provides a powerful, unified metrics platform for cloud-native applications.
1 min read
Custom Logs in Azure Monitor: Ingesting Any Data Source
Custom logs enable comprehensive observability across all your applications and services.
1 min read
Windows Event Collection: Comprehensive Monitoring with AMA
Comprehensive Windows Event collection is essential for security monitoring and compliance.
1 min read
Syslog Collection with Azure Monitor: Linux Log Management
Comprehensive syslog collection provides essential visibility into Linux system security and operations.
1 min read
Log Analytics Agent Deprecation: Migration Planning
Plan your migration early to ensure a smooth transition before the deprecation deadline.
1 min read
Azure Monitor Agent: The Future of Azure Monitoring
Azure Monitor Agent provides a unified, secure, and flexible foundation for all monitoring needs.
1 min read
Azure Data Collection Rules: Modern Log Ingestion
Data Collection Rules provide the flexibility and control needed for modern monitoring architectures.
1 min read
Azure Monitor Logs Dedicated Clusters: Enterprise-Scale Logging
Dedicated clusters provide enterprise-grade security and scale for Azure Monitor Logs.
1 min read
Azure Managed Grafana Preview: Enterprise Observability
Azure Managed Grafana provides enterprise observability with seamless Azure integration and reduced operational overhead.
2 min read
Data Observability: Monitoring Your Data Pipelines
Data observability is the capability that answers "is my data healthy right now?" the same way application observability answers "is my application healthy…
1 min read
Model Monitoring in Production: Detecting Drift Before It's Too Late
Model monitoring in 2021 became non-negotiable for production ML. The tools improved, but the discipline of continuous monitoring is what separates…
1 min read
OpenTelemetry with Azure: Vendor-Neutral Observability
OpenTelemetry provides a future-proof approach to observability. By using vendor-neutral instrumentation with Azure Monitor as the backend, you get the…
1 min read
Azure Monitor Application Insights: Deep Observability for Applications
Application Insights provides the deep observability needed for modern applications. Combined with Azure Monitor's broader capabilities, it enables…
2 min read
Cross-Workspace Queries in Azure Monitor
Cross-workspace queries in KQL let you write a single query that spans multiple Log Analytics workspaces—essential when your monitoring architecture splits…
2 min read
Log Analytics Workspace Design for Enterprise Kubernetes
Log Analytics workspace design is one of those infrastructure decisions that feels low-stakes until your bill arrives or you hit a query that crosses a…
2 min read
Deep Dive into Azure Monitor for Containers
Azure Monitor for Containers (the Container Insights feature) is the native Azure observability solution for AKS that doesn't require running your own…
2 min read
Building Effective Grafana Dashboards for AKS
Grafana is the visualisation layer that makes Prometheus metrics interpretable at a glance—and for AKS, the starting point is the community dashboards that…
2 min read
Prometheus Metrics Collection in AKS
Prometheus became the observability standard for Kubernetes because its data model—time-series metrics with labels—maps naturally to the dynamic…
2 min read
Comprehensive AKS Monitoring with Container Insights
Container Insights is the Azure Monitor feature that closes the observability gap for AKS clusters—without it, you have Kubernetes metrics available in the…
1 min read
Query Store Deep Dive: Your Database's Flight Recorder
Query Store transforms database performance troubleshooting from guesswork into data-driven analysis, making it an indispensable tool for maintaining…
1 min read
Configuring Azure Service Health Alerts
Service Health tracks three types of events: Service Issues : Active problems affecting Azure services Planned Maintenance : Upcoming maintenance that might…
1 min read
Monitoring Azure Resource Health for Proactive Operations
Resource Health reports four possible states: Available : Resource is healthy and operating normally Unavailable : Resource is not healthy due to Azure or…
1 min read
Working with Azure Activity Logs for Audit and Compliance
Activity Logs include several categories: Administrative : Resource management operations (create, update, delete) Security : Security Center alerts and…
1 min read
Configuring Azure Diagnostic Settings for Comprehensive Logging
Azure resources produce several types of diagnostic data: Platform Metrics : Numerical performance data Resource Logs : Detailed operational logs (formerly…
1 min read
Publishing Custom Metrics to Azure Monitor
There are two ways to publish custom metrics: Application Insights SDK : For application level metrics Azure Monitor REST API : For any metric source Use…
1 min read
Deep Dive into Azure Monitor Metrics
Azure Monitor collects two types of metrics: Platform Metrics : Automatically collected from Azure resources Custom Metrics : Application specific metrics…
1 min read
Container Monitoring on Azure: AKS Insights and Beyond
Container Insights is the built in monitoring solution for AKS: This deploys: OMS agent as DaemonSet Metrics collection Log forwarding to Log Analytics…
1 min read
Prometheus with Azure Monitor: Unified Observability
Azure Monitor for Prometheus addresses these while maintaining compatibility.
1 min read
Azure Managed Grafana: Visualization for Cloud Observability
Grafana on Azure became substantially easier when Azure Managed Grafana reached general availability. Before that, teams ran Grafana on AKS or a VM, managed…
1 min read
InfluxDB on Azure: Deploying Time-Series Infrastructure
For development or small workloads: For production workloads, use AKS with persistent volumes:
1 min read
Deep Dive into Application Insights
Application Insights is the monitoring tool that pays you back in proportion to how much you invest in it. Out of the box you get…
1 min read
Mastering Log Analytics Queries with KQL
KQL is the query language I've spent the most hours in this year that most developers have never heard of. If you use Log Analytics, Sentinel, Application…
1 min read
Proactive Monitoring with Azure Monitor Alerts
Azure Monitor supports several alert types: Metric alerts Based on metric values crossing thresholds Log alerts Based on Log Analytics query results…
1 min read
Azure Log Analytics: Central Logging Platform
Log Analytics is the database I've spent the most hours in this year that nobody calls a database. It's where every Azure diagnostic, AppInsights trace…
1 min read
Azure Monitor Workbooks: Custom Visualizations
Every dashboard request that hit my inbox in 2019 had the same wishlist: parameters at the top, charts that talk to each other, KQL on the inside, and…
1 min read
Application Insights: Full-Stack Application Monitoring
Application Insights is essential for production observability.
1 min read
Azure Monitor Alerts and Action Groups
Alerts should be actionable. If you can't do anything about it, it's noise.
1 min read
Application Performance Monitoring with Azure Application Insights
First thing I do on any new Azure-hosted application I'm asked to look at: open Application Insights and see what's actually happening. Half the time, "the…