I would talk about how Intercom built an important machine learning product, the interplay between the technical and product side of delivering a successful ML product.
Many people are interested in machine learning, but how do you build a ML product that actually ships? I’ll discuss the complexities you run into when building ML products above normal software development.
I’ll give some techniques and strategies for overcoming these, and take the audience through the timeline of how Intercom built a successful ML product. (& we built it in Python, even though Intercom is normally all Ruby!)
Track 1 Session 1, Saturday
Como arruinar el juego desde el inicio.
Se recomienda usar audífonos/auriculares.