Question: looking at the article's "AI Product Management" infographic, one item includes the phrase, "Adopt a bread-first approach to building a product". Is that supposed to be "breadth-first"?
It means to go for revenue first :-)
Lessons for Building AI-Driven Products
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Re: Lessons for Building AI-Driven Products
#12Could be better written but here are my thoughts on the same topic from earlier last year: https://medium.com/towards-data-science/hard-earned-advice-f...
Re: Lessons for Building AI-Driven Products
#13Earlier quoted context omitted.
Agreed, we've also written about why the best use of your time is to start with a baseline (even in Computer Vision!) https://blog.insightdatascience.com/always-start-with-a-stup...
And before a baseline, start with formulating a problem in a way that’s applicable to a real world product. Not just “image classification” but “image classification to improve QA in underwater basket weaving, a 10 billion dollar industry, from which we could generate $100m/yr in revenue by selling a product that does A, B, and C to an estimated 100k customers each of which would have $1000 LTV”. I’m in an AI startup…
I'd love to hear about your experiences — email in my profile.
I've been doing some writing and speaking on why AI seduces founders into pursuing lots of cool ideas instead of marketable products. I'd love another data point.
Could do it anonymously if you'd like, or hold off until after you bail.
Re: Lessons for Building AI-Driven Products
#14Earlier quoted context omitted.
And before a baseline, start with formulating a problem in a way that’s applicable to a real world product. Not just “image classification” but “image classification to improve QA in underwater basket weaving, a 10 billion dollar industry, from which we could generate $100m/yr in revenue by selling a product that does A, B, and C to an estimated 100k customers each of which would have $1000 LTV”. I’m in an AI startup…
I'd love to hear about your experiences — email in my profile. I've been doing some writing and speaking on why AI seduces founders into pursuing lots of cool ideas instead of marketable products. I'd love another data point. Could do it anonymously if you'd like, or hold off until after you bail.
[deleted]
Re: Lessons for Building AI-Driven Products
#15As someone who is a product manager in the commercialized AI SaaS space, the most important pieces of feedback I would give a new PM here: 1)Don't let your -brilliant- colleagues try to force their -brilliantly complex- solution of a problem - clearly define market problems, and don't let the team try to go the route of trying to force fit a solution to a market problem. Market problems come first. 2)Frame the market…
>Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.
This cannot be stressed enough, optimism bias will always push the scientist towards the 'more interesting/complete/new' method and model, but a seasoned practitioner will have the discipline to always establish a baseline (<1 days work).
Re: Lessons for Building AI-Driven Products
#16Their lesson, "Build Breadth-first (Data/Pipeline/Model) instead of Depth-first (AI model)" is insightful. Once the pipeline/etc. are built perhaps the AI part is not even needed any more. Finding this out early can save you a lot of trouble. Another perspective, in which we talk about on integrating AI into existing workflows, among other lessons: Smith, Reid G., and Joshua Eckroth. "Building AI Applications: Yester…
Not only that, but breadth-first makes it easy to: a) Iterate much faster on models; b) Keep better track of results and code and models, which is already quite difficult in ML; c) Enable other teams to work on other parts of the pipeline, in parallel.
Re: Lessons for Building AI-Driven Products
#17As someone who is a product manager in the commercialized AI SaaS space, the most important pieces of feedback I would give a new PM here: 1)Don't let your -brilliant- colleagues try to force their -brilliantly complex- solution of a problem - clearly define market problems, and don't let the team try to go the route of trying to force fit a solution to a market problem. Market problems come first. 2)Frame the market…
In my experience, simplifying a solution over time is the best way to ensure its adoption and long-term success. That applies both to UX/UI and the supporting computation/ML.
Re: Lessons for Building AI-Driven Products
#18A lot of good comments in this thread.
I am a data scientist and of course I love playing with the latest deep learning frameworks, but If I'm putting an ML model in production I want something that: will pass QA, is easy to interpret, will run in real-time and for those requirements it's hard to beat random forest, boosted trees, logistic regression and naive bayes
Re: Lessons for Building AI-Driven Products
#19Can anyone here offer advice for doing anomaly detection in distributed systems?
I'm not looking for advice on which models to use, per se. I'm more interested in how to go about things as a single-person team (building data warehousing infrastructure, etc.)