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Maybe odd question, but: would you recommend taking this course if my goal is to build (and sell) products leveraging ML? (e.g. SaaS) As in, with the pace of i
by frabia 4y ago
Maybe odd question, but: would you recommend taking this course if my goal is to build (and sell) products leveraging ML? (e.g. SaaS)
As in, with the pace of improvements from other AI startups and general availability of their APIs (e.g. GPT-4), is there a specific advantage (aside from maybe cost) to learning to build my own models? Or is the course more suitable for people wanting to become ML engineers (or similar) and to find a job as such?
Thanks in advance
- jph00 4y agoPart 1 would probably be best for that: https://course.fast.ai https://course.fast.ai . Lots of alums have gone on to do what you describe -- it's probably a good idea to have the level of understanding introduced there even if you mainly use external APIs. Part 2 is more for folks who want to go deeper in order to try new research ideas, optimize their models beyond standard approaches, etc.
- bravura 4y agoJust my two cents, as someone with 8000 academic citations and one AI SaaS exit, on AI entrepreneurship: The more time you spend on marketing, the better. We found that as our AI got worse, our product got better.
- frabia 4y agoThanks for your reply, though I'm not sure if I get your comment correctly. As in, I agree that marketing is super important but if I had an idea about a certain SaaS product that requires a certain ML model, I need to decide either to build it myself, or using somebody else's APIs. > We found that as our AI got worse, our product got better. Interesting. Could you elaborate on this too?
- bravura 4y agoI guess my last sentence was confusing. What I meant was that we fell into the common trap that scientists want to do as fancy science as possible when they leave academia and enter the startup world. Because there's an issue of pride and desire for uniqueness. Whereas good business is not like art, where the mandate is to be as creative and unique as possible. The analogy from sports (which I don't watch) applicable to business is that teams just copy each other's plays and focus on out-executing each other. So, specifically, our startup started with the idea that high-brow tech would be a key differentiator and give the best user experience. This is the common story trotted out by survivorship bias stories that make good tech news articles. Whereas making cuts to our R&D time and focusing on UI/UX and working with simpler science ultimately led to better product. From consulting, sales, and corporate work, one learns that the dirty secret of big-iron large tech companies is that all stuff sold as ML is just nicely packaged logistic regression. Or it was was five years ago. Nowadays I guess it would MAYBE be transformers, but the point being that off-the-shelf ML with solid data engineering work is what drives 99% of good products. Rarely is it truly innovative tech. I think Pete Skomoroch was the one that joked: "People say I'm a data scientist. I'm actually a data plumber." I guess one could take this lesson from science. What you learn from a good PhD advisor is: a) read the latest work b) note the simple baseline approach constantly trashed as scoring 2% worse than the sophisticated intricate new things proposed and c) implement the simple baseline. Achieve impact not by hillclimbing on a standard metric but define a new problem or arbitrage insights from adjacent fields, etc.
- frabia 4y agoI see. Much clearer! Thanks for the explanation.
- lmpdev 4y agoCould you elaborate on your last sentence?
- tomcam 4y ago> We found that as our AI got worse, our product got better. I’m not smart enough to decode this. I can imagine multiple conflicting answers.