Useful methodology to expand the data science toolkit
Data science is fundamental to Pinpoint’s application. But, like most startups, we are still in the process of building out our data science architecture; how we load data, store models/runtime data, execute scripts, and output results. Truthfully, our architecture and setup will never be “complete” because it should — and will — evolve as we expand and enhance our project portfolio. However, there are some concepts and components that we leverage today that would be useful in any data science environment. This blog covers the libraries, methods, and technical code logic to help enhance the daily workflow for data scientists based on what we have found useful for our own architecture at Pinpoint.
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