SciLifeLab Serve strives to be a platform that offers a suite of tools for researcher building and using machine learning models. While we do not offer hardware resources for training machine learning models (see NAISS resources for access to clusters for ML training in Sweden), you can use our service to track your machine learning experiments. This is made possible through integration of the most popular solution for experiment tracking - the open source suite MLFlow.

You can track your machine learning work through SciLifeLab Serve regardless of where exactly you are running training and analyses, it can be on your own computer, your research group machine, or the national clusters offered by NAISS. When you create an MLFlow instance through the Serve interface you will receive credentials with which you can connect to our servers and send metrics and artifacts. These metrics and artifacts alongside the other features become available in the graphical user interface of your MLFlow instance on SciLifeLab Serve.

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