1 min read
Data Quality Work That Actually Sticks: separating incident response from root-cause fixes
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
Observability
39 articles
1 min read
Observability for Data Products: turning data quality from blame into process
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Observability for Data Products: using failure budgets for data reliability
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
1 min read
Data Quality Work That Actually Sticks: choosing the minimum useful set of quality signals
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
Operational Data Quality Notes: separating incident response from root-cause fixes
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
Data Quality Work That Actually Sticks: choosing the minimum useful set of quality signals
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
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LLM Observability: What Actually Matters
Latency: P50, P95, P99 response times Cost: Per query, per user, per day Quality: Response relevance, accuracy
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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
Monitoring AI Applications with Azure Monitor and Application Insights
Configure alerts for latency spikes, error rate increases, and token usage anomalies to catch issues before they impact users.
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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…
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AI Monitoring Dashboards: Visualizing AI System Health
Comprehensive monitoring dashboards enable proactive AI operations management.
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AI Observability: Monitoring LLM Applications in Production
Comprehensive observability enables continuous improvement of AI applications.
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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.
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Phoenix Observability: Local-First LLM Tracing
Phoenix provides powerful local-first observability that keeps your data private while offering the visualization and analysis capabilities needed for…
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Weights and Biases for LLM Applications: Complete Guide
Weights & Biases provides a comprehensive platform for LLM observability, evaluation, and collaboration. Its strength lies in combining experiment tracking…
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LangSmith Alternatives: Choosing the Right LLM Observability Platform
The best tool depends on your specific needs. Start with the simplest option that meets your requirements, and upgrade as your needs grow.
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OpenTelemetry for AI: Standard Observability for LLM Applications
OpenTelemetry provides a standardized way to instrument AI applications, ensuring your observability data is portable across different backends and tools.
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Tracing Frameworks for AI: Distributed Tracing in LLM Applications
Effective tracing reveals the inner workings of AI applications, helping you understand performance bottlenecks, cost drivers, and error sources across your…
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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…
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Audit Logging for AI Agents: Complete Activity Tracking
Comprehensive audit logging is essential for AI agents in production. It enables debugging, ensures compliance, and provides the visibility needed to build…
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Tracing and Debugging AI Applications
Debugging AI applications is different from traditional software. Today I'm exploring tracing and debugging techniques for production AI systems.
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Monitoring AI Systems in Production
AI systems require specialized monitoring beyond traditional application metrics. This guide covers comprehensive observability for production AI.
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Tracing and Debugging LLM Applications
Advanced techniques for tracing and debugging complex LLM applications in production.
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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…
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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…
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Azure Monitor Agent: The Future of Azure Monitoring
Azure Monitor Agent provides a unified, secure, and flexible foundation for all monitoring needs.
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Azure Managed Grafana Preview: Enterprise Observability
Azure Managed Grafana provides enterprise observability with seamless Azure integration and reduced operational overhead.
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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…
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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…
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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…
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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…
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Prometheus with Azure Monitor: Unified Observability
Azure Monitor for Prometheus addresses these while maintaining compatibility.
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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
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…
2 min read
Building Interactive Dashboards with Azure Monitor Workbooks
Operational dashboards in Azure used to mean Power BI, a custom data export, and a refresh schedule nobody could remember. Workbooks killed all of that for…
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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…