13 posts · page 1 of 1
Learn to evaluate RAG pipelines with Recall@k, MRR, NDCG for retrieval and faithfulness, relevance, hallucination rate for generation. Includes RAGAS setup.
Learn the essential Kafka metrics to monitor, alerting strategies, operational tools like Prometheus and Grafana, and capacity planning for production clusters.
Master every classification and regression metric from accuracy and F1 to AUC-ROC and confusion matrices, with Python code and guidance on when to use each.
Understand the three pillars of observability. Learn how logs, metrics, and distributed traces work together to give you full visibility into production systems.
Master LLM evaluation with automated metrics like BLEU and ROUGE, plus human evaluation frameworks for measuring quality, safety, and reliability.
Design a time-series metrics pipeline like Prometheus or Datadog. Covers pull vs. push collection, aggregation, downsampling, storage engines, and alerting at scale.
Build a meaningful CloudWatch setup with custom metrics, composite alarms, and dashboards that catch real incidents without paging on noise.
A tour of the modern observability stack: metrics, logs, traces, and events. Learn how the pillars fit together and how to choose tooling without drowning in dashboards.
Understand how HPA decides when to add or remove pods, the metrics it can scale on, and the tuning knobs that prevent flapping and runaway scaling.
A thorough look at the confusion matrix: how to read it, the metrics it produces, and how to use it to diagnose classifier behavior beyond a single accuracy number that often hides what is going wrong.
Decode precision, recall, F1, and accuracy with concrete intuition, threshold tuning, and PR vs ROC curve guidance for imbalanced data.
Measure RAG quality with recall@k, MRR, context precision, faithfulness, and answer relevancy so you can iterate on data, not vibes.
Line, branch, and mutation coverage explained. Learn what each metric tells you, what it hides, and how to use coverage without gaming it.