It's flexible, relatively intuitive and continuously improving, built by a team that's eager to understand their users' needs.

The fact that I cane keep track of my experiments easily: - They are all on one place - I can compare between runs - I can log nearly everything I want even when tuning with optuna I can see all the nice metadata and visulizations. -

Neptune Ai, allows to store practically any necessary data, based on its way of storing data and metadata, it allows complete traceability in a simple way. It has an easy to use UI, which exposes, in a simple and useful way, practically all the necessary information, allowing some interesting customisations, such as being able to add/remove custom visualisations only with the necessary data/columns. It also has a Python SDK which is quite easy to use, allowing to adapt to the use case in a quite flexible way, as Neptune provides an accessible data and metadata container, so integration with other tools is quite feasible. On the support side, they are usually quite quick to respond and provide a lot of help. Another very interesting point is that the documentation is quite extensive and has a lot of examples.