Logging at Pulze.ai
Introduction
Observability is key to the smooth functioning and optimization of machine learning models, especially when managing large language models (LLMs) in modern machine learning applications. As part of the LLMOps stack, Pulze.ai plays an essential role in enhancing observability by providing robust logging features. Every request, successful or failed, is logged and can be retrieved for inspection. This detailed logging service allows you to have a comprehensive understanding of the interactions with your models. The logs are made available for your labeling team or annotators to sift through, rate them, and even provide feedback. This feedback is vital as it is leveraged to fine-tune your application further. The process of rating each log entry and providing feedback is emphasized at the beginning and throughout the Pulze.ai service. Moreover, we are excited to announce that an SDK will soon be available, enabling you to manage your logs programmatically for a streamlined experience.Pulze.ai’s Logging
Pulze.ai logs provide invaluable insights into the interactions between your users and your applications. They record crucial information such astimestamp
, model used
, latency
, cost
, prompt
, and response
for every request, regardless of whether it was successful or not.
This provides you with a rich dataset that can be used to analyze the performance of your models and identify any potential issues. With the added feature of logging failed requests, you can also use this information to improve the resilience and reliability of your models.