Integrations
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.
New Releases
🚀 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.
The release of aimlflow sparked user curiosity, a tool that facilitates seamless integration of a powerful experiment tracking user...
We are thrilled to unveil aimlflow, a tool that allows for a smooth integration of a robust experiment tracking UI with MLflow logs! 🚀
A retrospective look at the past year! Significant improvements enhanced the functionality and usability of Aim for tracking machine learning experiments for both small and large scale projects.
We are excited to announce the release of aimlflow, an integration that helps to seamlessly run a powerful experiment tracking UI on MLflow logs! 🎉
