Architecture for operationalizing data science analytics
In the previous section, we saw how PixieApps combined with the PixieDust display framework offer an easy way to build powerful dashboards that connect directly with your data analytics, allowing for rapid iterations between the algorithms and the user interface. This is great for rapid prototyping, but Notebooks are not suitable to be used in a production environment where the target persona is the line of business user. One obvious solution would be to rewrite the PixieApp using a traditional three tiers web application architecture, for example, as follows:
- React (https://reactjs.org) for the presentation layer
- Node.js for the web layer
- A data access library targeted at the web analytics layer for machine learning scoring or running any other analytic jobs
However, this would provide only a marginal improvement over the existing process, which would consist only, in this case, of the ability to do iterative implementation with the...
