
Sign up to save your podcasts
Or


Today we’re joined by Francesc Joan Riera, an applied machine learning engineer at The LEGO Group.
In our conversation, we explore the ML infrastructure at LEGO, specifically around two use cases, content moderation and user engagement. While content moderation is not a new or novel task, but because their apps and products are marketed towards children, their need for heightened levels of moderation makes it very interesting.
We discuss if the moderation system is built specifically to weed out bad actors or passive behaviors if their system has a human-in-the-loop component, why they built a feature store as opposed to a traditional database, and challenges they faced along that journey. We also talk through the range of skill sets on their team, the use of MLflow for experimentation, the adoption of AWS for serverless, and so much more!
The complete show notes for this episode can be found at twimlai.com/go/534.
By Sam Charrington4.7
422422 ratings
Today we’re joined by Francesc Joan Riera, an applied machine learning engineer at The LEGO Group.
In our conversation, we explore the ML infrastructure at LEGO, specifically around two use cases, content moderation and user engagement. While content moderation is not a new or novel task, but because their apps and products are marketed towards children, their need for heightened levels of moderation makes it very interesting.
We discuss if the moderation system is built specifically to weed out bad actors or passive behaviors if their system has a human-in-the-loop component, why they built a feature store as opposed to a traditional database, and challenges they faced along that journey. We also talk through the range of skill sets on their team, the use of MLflow for experimentation, the adoption of AWS for serverless, and so much more!
The complete show notes for this episode can be found at twimlai.com/go/534.

1,091 Listeners

172 Listeners

302 Listeners

343 Listeners

227 Listeners

205 Listeners

208 Listeners

305 Listeners

95 Listeners

516 Listeners

130 Listeners

92 Listeners

228 Listeners

632 Listeners

36 Listeners