AI Product Metrics - Dual Tracking Product and Model Performance
How to track both product metrics and model metrics for AI products, bridging the gap between business outcomes and technical performance.
Metrics
Azure Monitor - Full-Stack Observability Platform
Azure Monitor is Microsoft's comprehensive observability platform that collects, analyzes, and acts on telemetry from cloud and on-premises …
Comprehensive Model Evaluation Beyond Accuracy
How to evaluate ML models holistically, covering performance metrics, fairness analysis, robustness testing, and business impact assessment.
Evaluating RAG System Quality
How to measure and improve both retrieval quality and generation quality in RAG systems, with practical metrics and evaluation frameworks.
Grafana - Open-Source Observability Dashboards
Grafana is an open-source analytics and interactive visualization platform for monitoring data from Prometheus, Elasticsearch, InfluxDB, and …
InfluxDB - Purpose-Built Time Series Database
InfluxDB is an open-source time series database designed for high-write-throughput storage and real-time querying of timestamped data from …
KPI Framework for AI - Measuring AI Impact
A structured approach to defining, tracking, and reporting KPIs for AI initiatives across technical performance, business impact, and …
OKR Framework for AI - Objectives and Key Results
Applying OKRs to AI initiatives: setting measurable objectives, defining AI-appropriate key results, and aligning AI programs with business …
OpenTelemetry - Observability Framework Standard
OpenTelemetry is a vendor-neutral open-source observability framework for generating, collecting, and exporting telemetry data (traces, …
Prometheus
What Prometheus is, how it collects and stores metrics, and how it fits into cloud-native monitoring stacks.
Prometheus - Open-Source Monitoring and Alerting
Prometheus is an open-source systems monitoring and alerting toolkit designed for reliability, featuring a dimensional data model and …
RAG Evaluation
Methods and metrics for measuring the quality of Retrieval Augmented Generation systems, covering retrieval accuracy, generation …
Software Quality Assurance for AI/ML Projects
Quality planning, metrics, and gates adapted for AI and ML projects where outputs are probabilistic and data quality is a first-class …
Amazon CloudWatch - Monitoring and Observability for AI
Using Amazon CloudWatch for AI workloads: custom metrics for LLM cost and token usage, alarms for model quality, log insights for inference …
Observability
What observability means, the three pillars of logs, metrics, and traces, and why AI systems need specialized observability for token costs, …
Observability for AI Systems - Logs, Metrics, Traces
Applying the three pillars of observability to AI workloads: CloudWatch for metrics and alarms, Langfuse for LLM tracing, OpenTelemetry for …