Designing the UX of ML Products
And one more article this week: Why Amazon Won’t Dominate in Groceries
Designing the User Experience of ML Products
Three Principles: Expectations, Errors, And Trust!
Previously, I talked about challenges in managing machine learning (ML) products as it involves more experiments, iterations, and therefore more uncertainties. As a PM, you need to give engineers and data scientists enough space and flexibility to explore before deciding on the path going forward. But you also need to clearly define the objective functions and encourage the team to test early and often so you don’t lose track.
The same challenges apply when you are designing the user experience (UX) for your ML products. Over the last few months, I’ve been working with our UX team to gather customer inputs and improve the UX of our products. Here are the three most important lessons that we learned. Continue to read…
You might also like: How to Manage Machine Learning Products and How to Manage Machine Learning Products — Part II
Why Amazon Won’t Dominate in Groceries
More than two years after Whole Foods buy, grocery has yet to be “Amazoned.” And here’s why.
Grocery is the largest retail segment in the U.S., but its online penetration remains low: Only 3% of grocery sales happen online, compared to 30.2% for electronics and 27.4% for apparel. The reason? Groceries are particularly hard to handle with the kind of warehouse distribution model that an online retail powerhouse like Amazon relies on.
The company I work for provides machine learning-enabled robotics for picking products in warehouses, so the grocery challenge is a topic I keep a close eye on. And recently, there’s been some interesting experimentation on this front, with British online grocer Ocado trying out a whole new approach to the one Amazon has trialed so far. Continue to read…
