Experiment Tracking in Kubeflow Pipelines - neptune.ai

Experiment tracking has been one of the most popular topics in the context of machine learning projects. It is difficult to imagine a new project being developed without tracking each experiment's run history, parameters, and metrics. While some projects may use more "primitive" solutions like storing all the experiment metadata in spreadsheets, it is definitely not a good practice. It will become really tedious as soon as the team grows and schedules more and more experiments. Many mature and actively developed tools can help your team track machine learning experiments. In this article, I will introduce and describe some of these tools, including TensorBoard, MLFlow, and Neptune.ai,