This article discusses the importance of understanding and memorizing classification metrics in machine learning. The author shares their own experience and strategies for memorizing metrics such as accuracy, precision, recall, F1 score, and ROC AUC.
The article explains how to apply Friedman's h-statistic to understand if complex machine learning models use interactions to make predictions. It uses the artemis package and interprets the pairwise, overall, and unnormalised metrics.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
With the addition of profiling to OpenTelemetry, we expect continuous production profiling to hit the mainstream.
This article explains the differences between observability, telemetry, and monitoring, and how they work together to help teams understand and improve their software systems. It also discusses the benefits of using OpenTelemetry, a standard for creating and collecting telemetry for software systems, and Honeycomb's observability platform.
OpenTelemetry offers a standardized process for observability, but its functionality is a work in progress. Its usefulness depends on the observability tools and platforms used in conjunction with OpenTelemetry.
Langfuse is an open-source LLM engineering platform that offers tracing, prompt management, evaluation, datasets, metrics, and playground for debugging and improving LLM applications. It is backed by several renowned companies and has won multiple awards. Langfuse is built with security in mind, with SOC 2 Type II and ISO 27001 certifications and GDPR compliance.
Why evaluating LLM apps matters and how to get started
