Integrations
Explore step-by-step guide for LangChain Debugger, a tool designed for in-depth tracking and visualization of LangChain scripts. The guide features a hands-on example emphasizing its abilities to log LLMs prompts, tool inputs/outputs, and chain metadata, providing a clear understanding of its functionalities.
Discover AI observability with AimOS's LlamaIndex Observer. Essential insights for efficient AI model tracking and analysis. Check out our guide for observability solutions in the world of AI.
New Releases
We have moved the Aim 4.0 to a new repo AimOS and reinstated Aim to its previous version.
Discover AimOS: Open-source modular observability for AI systems. Easily log, connect and observe any parts of your AI Systems from experiments to production to prompts to AI system monitoring
Deploy Aim on Hugging Face Spaces using the Docker template. Aim empowers you to explore logs with interactive visualizations, easily compare training runs at scale and be on top of ML development insights.
As AI systems get increasingly complex, the ability to effectively debug and monitor them becomes crucial. Use Aim to easily trace complex AI systems.
Tutorials
You can now track your Prophet experiments with Aim! The recent Aim v3.16 release includes a built-in logger object for Prophet runs.
Combining Hugging Face and Aim to make machine learning experiments traceable, reproducible and easier to compare.
🚀 Aim v3.16 is out! Users will be able to view all Run messages right in the UI. Integration of Aim with TensorBoard, Hugging Face Datasets, Acme, Stable-Baselines3.
