Search Results for "world monitor"

...Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting.

...The platform provides tools for tracking model interactions, analyzing prompt behavior, and identifying issues such as hallucinations, latency problems, or unexpected responses. By collecting telemetry data from AI applications, LangWatch allows developers to understand how their systems perform in real-world usage scenarios. The platform includes dashboards that visualize model behavior, enabling teams to monitor trends in response quality and reliability over time. It also provides evaluation tools that allow developers to test prompts and compare outputs across different models or configurations. Through integration with popular AI development frameworks, LangWatch can be embedded directly into AI pipelines to provide continuous monitoring and evaluation.

...In fact, you can use and combine your preferred libraries without implementing any specific interface. Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Argilla is designed to close this gap, enabling you to iterate as much as you need.