I’ve found Neptune pretty easy to integrate. Although I don’t use it in the traditional way, I rely on it to track experiment evaluations and monitor the performance of our LLM-based applications. The tool is flexible and adapts to most of all types of tracking needs. Recently, I've had the need to map API call statuses, including error codes, and the process was seamless and fast. Another super positive aspect is the incredible customer support. We have regular meetings, and the team is always willing to listen to our requests and translate them into practical solutions. They consistently share valuable resources and try to meet our needs.

- Funcionalidad de filtro en ejecuciones dentro de un proyecto - Tableros personalizados y esquemas de tablas guardables - Despliegue en las instalaciones que funciona de manera confiable (GCP) - Contenido registrado está organizado bajo dominios dentro de ejecuciones

Neptune helps track experiment data with a simple interface. The dashboard shows key metrics, graphs, and lets you compare different model runs side by side. Our team uses it to manage thousands of machine learning experiments, from initial training through fine-tuning and final evaluation. The system handles large amounts of data well and loads results quickly. You can organize experiments into projects, tag important runs, and easily search through past results. It also lets you log both training metrics and evaluation scores in one place.