There are several tools to build ML dashboards and visulisations. Their focus is often on making it as simple as possible for a (Python) data scientist. Shipp ing them as part of our product means that other roles like frontend developers get involved. Aspects that ease development for one role, create pains for oth ers. We want to show how balance this using Altair, Vega and Vue.
Visualising the impact of machine learning models and parts of their resoning is an important component in building a successful data product. Often we use these visualisations to understand the effects and shortcoming of our own models to ourselves and our colleagues. The impact of the output of the machine learning task is often not bound to the team that develops these models but extends to either other departments of the same company or external customers. They need to understand the effects without knowing the internals of the model and without being able to run the model themselves. This might either be due to them not being so techsavy to run the tools or being external in such a sense that the model code itself is a (trade) secret to them.