The nuance of AI design patterns
Have you wondered how to create products that use AI/ML at its core?
Do you want to know the nuance of data, training, experience, and other trade-offs that are particular to AI/ML products?
The key is balancing a combination of the basics that are already proven and being aware of the heuristics that are currently being formed for new technology. These are frameworks you already know evolved to handle AI/ML and new ones that were created with AI/ML in mind specifically.
I’m planning on offering a cohort based course through Maven that will help answer all of these questions (and more)! Keep reading for more info…
My struggle and your gain
I’ve tried, struggled, experimented, failed, and succeeded at these questions over my career. This includes experience from GitHub, Google’s Core Machine Learning team, Google’s People + AI Guidebook, Cognizant, Facebook Reality Labs’ Portal device, IPsoft’s Amelia, and Philosophie’s design consulting work on AI.
Ever since my first work in AI patterns back in 2017 and the Design Thinking for AI workshop at the O’Reilly AI Conf in 2018 I’ve been iterating on these concepts. Even if “design thinking” has started to fall out of favor the methodologies that I used understand umbrella are still helpful.
I need your thoughts!
Before I launch the course, I’d love to see what I should focus on. If you are interested in this type of course please take a few minutes to sign up for notifications and answer questions about your background:
If you want to get a taste of what I’ll be talking about see some of my previous posts here:
