- A lot of machine learning startups initially feel a bit of “impostor syndrome” around competing with big companies, because (the argument goes), those companies have all the data; surely we can’t beat that!
- You can actually achieve great results in a lot of areas even with a relatively small data set, argue the guests on this podcast, if you build the right product on top of it.
- So how do you go about building the right product (beyond machine-learning algorithms in academic papers)?
- Because, observes moderator (and a16z board partner) Steven Sinofsky, “To achieve product market fit, there’s a whole bunch of stuff beyond a giant corpus of data, and the latest deep learning algorithm.”
- Machine learning is an ingredient, part of a modern software-as-a-service company; going beyond the hype, it’s really about figuring out the problem you’re trying to solve… and then figuring out where machine learning fits in (as opposed to the other way around).
Stream a16z Podcast: The Product Edge in Machine Learning Startups by a16z from desktop or your mobile device
@onwrd_: 🗣️🎧The Product Edge in #MachineLearning #Startups by @stevesi of @a16z w/ @jensenharris & @ajshankar #soundcloud
Your current browser isn’t compatible with SoundCloud.
Please download one of our supported browsers. Need help?