Building a Data Science Pipeline

2017 July 25

I attended Wolfram’s “Building a Data Science Pipeline” webinar today, presented by Abrita Chakravarty.

Here’s the diagram of the pipeline presented. Pretty conventional.

Wolfram Data Science Pipeline

(Image copyright Wolfram Research)

The primary example used was a simple recipe classification project: classifying list of ingredients in the pantry by cuisine type to determine which recipes might work with what is on hand. (Yes, a little forced, I know.) Chakravarty walked through importing data (in JSON format), doing a little tidying up, partitioning the data into training and testing sets, running a few iterations of training, testing, and interpreting results.

It was a decent overview of supervised learning for classification, but their (implicit) pitch was not sufficiently compelling to induce a change of toolkits.

If you’re interested in seeing the workbooks they provided, please let me know.

Tags: data science