Data visualization plays a crucial role in the everyday work of data scientists and ML engineers. It is important to present data in different visual forms at all stages of the ML process: when data scientists prepare the training data set, inspect how well the model converges during the training, and then when it is time to validate the trained model and analyze the results of inference. Existing data visualization tools available in JupyterLab are mostly limited to a static data representation and do not provide enough interactivity.

In this demo, we will look at how FireFly integrates with JupyterLab to enhance your AI/ML and astronomical data science processes by providing interactive visualization components integrated into the JupyterLab notebook. We will see how FireFly visualizations can be helpful for preliminary data inspection and cleaning, exploratory data analysis, feature engineering, and model inference results analysis. The demo will showcase JupyterLab FireFly extensions for displaying tabular data, FITS images, plotting charts, visualizing HiPS maps, or overlaying data on the reference images.