ML Related Products That Allow People to Make Sense of Things

2min Snip

00:00

Play full episode

Summary

Transcript

Episode notes

The explosion of startups that are pretty much auto ML you know throw throw your data in see. I've always thought of it more as like an actual pipeline so that ML engineers can push their models back to the product but maybe I've totally misinterpreted this one yeah no I don't think it depends on what type of model is being used. We have a huge visual visualization layer that's just called like basically it allows people to visualize and in three dimensions like what is someone's style and how are we measuring it. So for ML especially because it can become so complex so quickly there's like a huge numberlike huge opportunities to build these products that allow people to make sense of things otherwise would

Have you ever built a data-related "thing" — a dashboard, a data catalog, an experimentation platform, even — only to find that, rather than having the masses race to adopt it and use it on a daily basis, it gets an initial surge in usage… and then quietly dies? That's sorta' the topic of this episode. Except that's a pretty clunky and overly narrow summary. Partly, because it's a hard topic to summarize. But, data as a product and data products are the topic, and Eric Weber, the data scientist behind the From Data to Product newsletter, joined us for a discussion that we've been trying to make happen for months. It was worth the wait! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.